Abstract
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.
- [1] . 2019. PyTorch: An imperative style, high-performance deep learning library. arXiv (2019). https://arxiv.org/abs/1912.01703. Google ScholarDigital Library
- [2] . 2016. node2vec: Scalable feature learning for networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’16). ACM, 855–864. https://doi.org/10.1145/2939672.2939754 Google ScholarDigital Library
- [3] . 2018. Semi-supervised user geolocation via graph convolutional networks. In The Annual Meeting of the Association for Computational Linguistics (ACL’18). ACL, 2009–2019.Google Scholar
- [4] . 2017. Protein interface prediction using graph convolutional networks. In The International Conference on Neural Information Processing Systems. Curran Associates, Inc., 6530–6539. Google ScholarDigital Library
- [5] . 2009. Learning multiple layers of features from tiny images. (2009). https://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdf.Google Scholar
- [6] . 2018. Graph networks as learnable physics engines for inference and control. In The International Conference on Machine Learning (ICML’18), Vol. 80. PMLR, 4470–4479.Google Scholar
- [7] . 2020. Memory-based graph networks. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [8] . 2020. What graph neural networks cannot learn-depth vs width. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [9] . 2017. Cross-sentence N-ary relation extraction with graph LSTMs. Transactions of the Association for Computational Linguistics 5 (2017), 101–115. https://doi.org/10.1162/tacl_a_00049Google ScholarCross Ref
- [10] . 2020. Multi-MotifGAN (MMGAN): Motif-targeted graph generation and prediction. In The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20). IEEE, 4182–4186. https://doi.org/10.1109/ICASSP40776.2020.9053451Google ScholarCross Ref
- [11] . 2016. Structural-RNN: Deep learning on spatio-temporal graphs. In The International Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE, 5308–5317.Google ScholarCross Ref
- [12] . 2017. Attention is all you need. In The International Conference on Neural Information Processing (NIPS’17). Curran Associates, Inc., Long Beach, CA, 5998–6008. Google ScholarDigital Library
- [13] . 1970. An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal 49, 2 (1970), 291–307. https://doi.org/10.1002/j.1538-7305.1970.tb01770.xGoogle ScholarCross Ref
- [14] . 2019. KagNet: Knowledge-aware graph networks for commonsense reasoning. In The International Conference on Empirical Methods in Natural Language Processing (EMNLP’19). ACL, 2829–2839.Google ScholarCross Ref
- [15] . 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In The International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI Organization, 3634–3640. https://doi.org/10.24963/ijcai.2018/505 Google ScholarDigital Library
- [16] . 2019. Graph wavelet neural network. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [17] . 2019. Syntax-aware aspect level sentiment classification with graph attention networks. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’19). ACL, 5469–5477.Google ScholarCross Ref
- [18] . 2019. Certifiable robustness to graph perturbations. In The International Conference on Neural Information Processing Systems (NeurPS’19), Vol. 32. Curran Associates, Inc. Google ScholarDigital Library
- [19] . 2014. DeepWalk: Online learning of social representations. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’14). ACM, 701–710. https://doi.org/10.1145/2623330.2623732 Google ScholarDigital Library
- [20] . 2019. Hyperbolic attention networks. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [21] . 2020. MultiSage: Empowering GCN with contextualized multi-embeddings on web-scale multipartite networks. In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’20). ACM, 2434–2443. https://doi.org/10.1145/3394486.3403293Google ScholarDigital Library
- [22] . 2020. Heterogeneous network representation: A unified framework with survey and benchmark. IEEE Transactions on Knowledge and Data Engineering (2020). https://doi.org/10.1109/TKDE.2020.3045924Google ScholarCross Ref
- [23] . 2018. Stochastic training of graph convolutional networks with variance reduction. In The 35th International Conference on Machine Learning (ICML’18), Vol. 80. PMLR, 942–950.Google Scholar
- [24] . 2020. GraphFlow: Exploiting conversation flow with graph neural networks for conversational machine comprehension. In The International Joint Conference on Artificial Intelligence (IJCAI’20). IJCAI Organization, 1230–1236. https://doi.org/10.24963/ijcai.2020/171 Google ScholarDigital Library
- [25] . 2021. RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recommendation. In The World Wide Web Conference (WWW’21). 499–508. Google ScholarDigital Library
- [26] . 2018. Dual graph convolutional networks for graph-based semi-supervised classification. In The World Wide Web Conference (WWW’18). 499–508. Google ScholarDigital Library
- [27] . 2019. An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. In The International Conference on Computer Vision and Pattern Recognition (CVPR’19). IEEE, 1227–1236.Google ScholarCross Ref
- [28] . 2019. Graph hypernetworks for neural architecture search. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [29] . 2015. Going deeper with convolutions. In The IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’15). IEEE, 1–9. https://doi.org/10.1109/CVPR.2015.7298594Google ScholarCross Ref
- [30] . 2019. Weisfeiler and Leman go neural: Higher-order graph neural networks. In The Conference on Artificial Intelligence (AAAI’19). Association for the Advance of Artificial Intelligence, 4602–4609. https://doi.org/10.1609/aaai.v33i01.33014602 Google ScholarDigital Library
- [31] . 2020. GMAN: A graph multi-attention network for traffic prediction. In The Conference on Artificial Intelligence (AAAI’20). Association for the Advance of Artificial Intelligence.Google ScholarCross Ref
- [32] . 2017. MGAE: Marginalized graph autoencoder for graph clustering. In The International Conference on Information and Knowledge Management (CIKM’17). ACM, 889–898. https://doi.org/10.1145/3132847.3132967 Google ScholarDigital Library
- [33] . 1992. Spectral Graph Theory. American Mathematical Society.Google Scholar
- [34] . 2019. Heterogeneous graph neural network. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 793–803. https://doi.org/10.1145/3292500.3330961Google ScholarDigital Library
- [35] . 2018. Learning structural node embeddings via diffusion wavelets. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 1320–1329. Google ScholarDigital Library
- [36] . 2019. A graph-convolutional neural network model for the prediction of chemical reactivity. Chemical Science 10, 2 (2019), 370–377. https://doi.org/10.1039/C8SC04228DGoogle ScholarCross Ref
- [37] . 2020. Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning. arXiv (2020). https://arxiv.org/abs/2009.06946v1.Google Scholar
- [38] . 2018. Towards sparse hierarchical graph classifiers. arXiv (2018). https://arxiv.org/abs/1811.01287.Google Scholar
- [39] . 2016. Structural deep network embedding. In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’16). ACM, New York, NY, 1225–1234. https://doi.org/10.1145/2939672.2939753 Google ScholarDigital Library
- [40] . 2017. Graph-structured representations for Visual Question Answering. In The International Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 1–9.Google ScholarCross Ref
- [41] . 2019. Relational graph attention networks. arXiv (2019). https://arxiv.org/abs/1904.05811.Google Scholar
- [42] . 2017. Scene graph generation by iterative message passing. In The International Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 5140–5419.Google ScholarCross Ref
- [43] . 2018. Graph-to-sequence learning using gated graph neural networks. In The Annual Meeting of the Association for Computational Linguistics (ACL’18), Vol. 1. ACL, 273–283.Google Scholar
- [44] . 2019. Learning a SAT solver from single-bit supervision. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [45] . 2018. Adversarial attacks on neural networks for graph data. In The International Conference on Machine Learning (ICML’18). PMLR, 2847–2856. https://doi.org/10.1145/3219819.3220078Google ScholarDigital Library
- [46] . 2019. Adversarial attacks on graph neural networks via meta learning. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [47] . 2019. Certifiable robustness and robust training for graph convolutional networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 246–256. https://doi.org/10.1145/3292500.3330905 Google ScholarDigital Library
- [48] . 2015. Convolutional networks on graphs for learning molecular fingerprints. In The International Conference on Neural Information Processing Systems (NIPS’15). Curran Associates, Inc., 2224–2232. Google ScholarDigital Library
- [49] . 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine 30, 3 (2013), 83–98.Google ScholarCross Ref
- [50] . 2011. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis 30, 2 (2011), 129–150. https://doi.org/10.1016/j.acha.2010.04.005Google ScholarCross Ref
- [51] . 2017. Variational inference: A review for statisticians. Journal of the Americian Statistical Association 112, 518 (2017), 859–877. https://doi.org/10.1080/01621459.2017.1285773Google ScholarCross Ref
- [52] . 2020. A gentle introduction to deep learning for graphs. Neural Networks 129 (2020), 203–221. https://doi.org/10.1016/j.neunet.2020.06.006Google ScholarCross Ref
- [53] . 2019. DialogueGCN: A graph convolutional neural network for emotion recognition in conversation. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’19). ACL, 154–164.Google ScholarCross Ref
- [54] . 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’19), Vol. 1. ACL, 4171–4186. https://doi.org/10.18653/v1/N19-1423Google Scholar
- [55] . 2018. Exploiting semantics in neural machine translation with graph convolutional networks. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’18). ACL, 486–492.Google ScholarCross Ref
- [56] . 2018. Deep variational network embedding in Wasserstein space. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 2827–2836. https://doi.org/10.1145/3219819.3220052 Google ScholarDigital Library
- [57] . 2019. Robust graph convolutional networks against adversarial attacks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 1399–1407. Google ScholarDigital Library
- [58] . 2019. 3D point cloud generative adversarial network based on tree structured graph convolutions. In The International Conference on Computer Vision (ICCV’19). IEEE, 3859–3868.Google ScholarCross Ref
- [59] . 2019. Graph convolutional neural networks via scattering. Applied and Computational Harmonic Analysis 49, 3 (2019), 1046–1074. https://doi.org/10.1016/j.acha.2019.06.003Google ScholarCross Ref
- [60] . 2015. Neural machine translation by jointly learning to align and translate. In The International Conference on Learning Representations (ICLR’15).Google Scholar
- [61] . 2019. Variational recurrent neural networks for graph classification. arXiv (2019). https://arxiv.org/abs/1902.02721.Google Scholar
- [62] . 2019. Variational graph recurrent neural networks. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 10701–10711. Google ScholarDigital Library
- [63] . 2019. InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [64] . 2019. Explainability techniques for graph convolutional networks. arXiv (2019). https://arxiv.org/abs/1905.13686.Google Scholar
- [65] . 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs. In The Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 5425–5434.Google ScholarCross Ref
- [66] . 2017. Robust spatial filtering with graph convolutional neural networks. IEEE Journal of Selected Topics in Signal Processing 11, 6 (2017), 884–896.Google ScholarCross Ref
- [67] . 2019. Simplifying graph convolutional networks. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 6861–6871.Google Scholar
- [68] . 2019. Semi-supervised node classification via hierarchical graph convolutional networks. arXiv (2019). https://arxiv.org/abs/1902.06667v2.Google Scholar
- [69] . 2016. Multi-scale context aggregation by dilated convolutions. arXiv (2016). https://arxiv.org/abs/1511.07122v3.Google Scholar
- [70] . 2020. Dynamic graph convolutional networks. Pattern Recognition 97 (2020), No. 107000. https://doi.org/10.1016/j.patcog.2019.107000Google ScholarCross Ref
- [71] . 2008. The graph neural network model. IEEE Transactions on Neural Networks 20, 1 (2008), 61–80. https://doi.org/10.1109/TNN.2008.2005605 Google ScholarDigital Library
- [72] . 2009. Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks 20, 1 (2009), 81–102. Google ScholarDigital Library
- [73] . 2019. Graph U-Nets. In The International Conference on Machine Learning, Vol. 97. PMLR, 2083–2092.Google Scholar
- [74] . 2020. Graph neural architecture search. In The International Joint Conference on Artificial Intelligence (IJCAI’20). IJCAI Organization, 1403–1409. https://doi.org/10.24963/ijcai.2020/195 Google ScholarDigital Library
- [75] . 2017. Densely connected convolutional networks. In The IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 2261–2269. https://doi.org/10.1109/CVPR.2017.243Google ScholarCross Ref
- [76] . 2020. Generalization and representational limits of graph neural networks. In The 37th International Conference on Machine Learning (ICML’20), Vol. 119. PMLR, 3419–3430.Google Scholar
- [77] . 2005. A new model for learning in graph domains. In The International Joint Conference on Neural Networks (IJCNN’05), Vol. 2. 729–734.Google ScholarCross Ref
- [78] . 2021. Training graph neural networks with 1000 layers. In The International Conference on Machine Learning (ICML’21). PMLR, 338–348. https://doi.org/10.1145/3394486.3403076Google Scholar
- [79] . 2019. DeepGCNs: Can GCNs go as deep as CNNs. In The International Conference on Computer Vision (ICCV’19). IEEE, 9267–9276.Google ScholarCross Ref
- [80] . 2019. Invariant and equivariant graph networks. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [81] . 2018. Adversarial attack on graph structured data. In The International Conference on Machine Learning (ICML’18), Vol. 80. PMLR, 1115–1124.Google Scholar
- [82] . 2020. Iterative graph self-distillation. arXiv (2020). https://arxiv.org/abs/2010.12609v1.Google Scholar
- [83] . 2020. Explainability in graph neural networks: A taxonomic survey. arXiv (2020). https://arxiv.org/abs/2012.15445.Google Scholar
- [84] . 2020. StructPool: Structured graph pooling via conditional random fields. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [85] . 2019. Graph neural networks with generated parameters for relation extraction. In The Annual Meeting of the Association for Computational Linguistics. ACL, 1331–1339.Google Scholar
- [86] . 2020. Contrastive multi-view representation learning on graphs. In The 37th International Conference on Machine Learning, Vol. 119. PMLR, 4116–4126.Google Scholar
- [87] . 2020. Spectral graph attention network. arXiv (2020). https://arxiv.org/abs/2003.07450.Google Scholar
- [88] . 2020. Geom-GCN: Geometric graph convolutional networks. In The International Conference on Learning Representations (ICLR’20).Google Scholar
- [89] . 2019. Conditional random field enhanced graph convolutional neural networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 276–284. https://doi.org/10.1145/3292500.3330888 Google ScholarDigital Library
- [90] . 2018. Large-scale learnable graph convolutional networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 1415–1424. https://doi.org/10.1145/3219819.3219947 Google ScholarDigital Library
- [91] . 2020. Open graph benchmark: Datasets for machine learning on graphs. In The International Conference on Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 22118–22133.Google Scholar
- [92] . 2019. Heterogeneous graph attention networks for semi-supervised short text classification. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’19). ACL, 4821–4830.Google ScholarCross Ref
- [93] . 2014. Generative adversarial nets. In The International Conference on Neural Information Processing Systems (NIPS’14), Vol. 27. 2672–2680. Google ScholarDigital Library
- [94] . 2004. Kernel k-means, spectral clustering and normalized cuts. In The ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD’04), Vol. 32. ACM, 551–556. https://doi.org/10.1145/1014052.1014118 Google ScholarDigital Library
- [95] . 2007. Weighted graph cuts without eigenvectors: A multilevel approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 11 (2007), 1944–1957. https://doi.org/10.1109/TPAMI.2007.1115 Google ScholarDigital Library
- [96] . 2019. Hyperbolic graph convolutional neural networks. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 4868–4879. Google ScholarDigital Library
- [97] . 2016. Diffusion-convolutional neural network. In The International Conference on Neural Information Processing Systems (NIPS’16). Curran Associates, Inc., 1993–2001. Google ScholarDigital Library
- [98] . 2015. Towards AI-Complete question answering: A set of prerequisite toy tasks. arXiv (2015). https://arxiv.org/abs/1502.05698v1.Google Scholar
- [99] . 2017. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146 (2017), 1038–1049. https://doi.org/10.1016/j.neuroimage.2016.09.046Google ScholarCross Ref
- [100] . 2019. Inferring Javascript types using graph neural networks. arXiv (2019). https://arxiv.org/abs/1905.06707.Google Scholar
- [101] . 2018. Topology adaptive graph convolutional networks. arXiv (2018). https://arxiv.org/abs/1710.10370.Google Scholar
- [102] . 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888–905. https://doi.org/10.1109/34.868688 Google ScholarDigital Library
- [103] . 2018. Stochastic training of graph convolutional networks with variance reduction. In The International Conference on Machine Learning (ICML’18). PMLR, 942–950.Google Scholar
- [104] . 2020. Towards high-fidelity 3d face reconstruction from in-the-wild images using graph convolutional networks. In The International Conference on Computer Vision and Pattern Recognition (CVPR’20). IEEE.Google ScholarCross Ref
- [105] . 2018. GaAN: Gated attention networks for learning on large and spatiotemporal graphs. In The International Conference on Uncertainty in Artificial Intelligence (UAI’18). PMLR, No. 139.Google Scholar
- [106] . 2018. Graph R-CNN for scene graph generation. In The European Conference on Computer Vision (ECCV’18). Springer.Google ScholarCross Ref
- [107] . 2021. Sub-graph contrast for scalable self-supervised graph representation learning. In The International Conference on Data Mining (ICDM’21). IEEE.Google Scholar
- [108] . 2020. Graph-Bert: Only attention is needed for learning graph representations. arXiv (2020). https://arxiv.org/abs/2001.05140.Google Scholar
- [109] . 2018. Graph convolutional policy network for goal-directed molecular graph generation. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Assoicates, Inc., Montreal, Quebec, Canada, 6412–6422. Google ScholarDigital Library
- [110] . 2019. G2SAT: Learning to generate SAT formulas. In The International Conference on Neural Information Processing Systems (NeurPS’18). Curran Associates, Inc., 10552–10563. Google ScholarDigital Library
- [111] . 2018. GraphRNN: Generating realistic graphs with deep auto-regressive models. In The International Conference on Machine Learing (ICML’18), Vol. 80. PMLR, 5708–5717.Google Scholar
- [112] . 2019. Position-aware graph neural networks. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 7134–7143.Google Scholar
- [113] . 2020. LambdaNet: Probabilistic type inference using graph neural networks. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [114] . 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. In The International Conference on Learning Representations (ICLR’18).Google Scholar
- [115] . 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020), 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001Google ScholarCross Ref
- [116] . 2019. GEAR: Graph-based evidence aggregating and reasoning for fact verification. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 892–901.Google Scholar
- [117] . 2018. DeepInf: Social influence prediction with deep learning. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). 234, 2110–2119. Google ScholarDigital Library
- [118] . 2020. GCC: Graph contrastive coding for graph neural network pre-training. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’20). ACM, 1150–1160. https://doi.org/10.1145/3394486.3403168Google ScholarDigital Library
- [119] . 2019. Symmetric graph convolutional autoencoder for unsupervised graph representation learning. In The International Conference on Computer Vision (ICCV’19). IEEE, Seoul, Korea, 6519–6528.Google ScholarCross Ref
- [120] . 2014. Spectral networks and locally connected networks on graphs. In The International Conference on Learning Representations (ICLR’14).Google Scholar
- [121] . 2019. Predict the propagate: Graph neural networks meet personalized pagerank. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [122] . 2019. Attention models in graphs: A survey. ACM Transactions on Knowledge Discovery from Data 13, 6 (2019), No. 62. Google ScholarDigital Library
- [123] . 2018. Graph classification using structural attention. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 1666–1674. https://doi.org/10.1145/3219819.3219980 Google ScholarDigital Library
- [124] . 2001. Conditional random field: Probabilistic models for segmenting and labeling sequence data. In The International Conference on Machine Learning (ICML’01), Vol. 97. PMLR, 282–289. Google ScholarDigital Library
- [125] . 2020. Graph pooling with representativeness. In the IEEE International Conference on Data Mining (ICDM). IEEE, 20424165. https://doi.org/10.1109/ICDM50108.2020.00039Google Scholar
- [126] . 2019. DEMO-Net: Degree-specific graph neural networks for node and graph classification. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 406–415. Google ScholarDigital Library
- [127] . 2019. Self-attention graph pooling. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 3734–3743.Google Scholar
- [128] . 2017. Neural message passing for quantum chemistry. In The International Conference on Machine Learning (ICML’17), Vol. 70. PMLR, 1263–1272. Google ScholarDigital Library
- [129] . 2015. Improved semantic representations from tree-structured long short-term memory networks. In The Annual Meeting of the Association for Computational Linguistics (ACL’15), Vol. 1. ACL, 1556–1566.Google Scholar
- [130] . 2016. Deep residual learning for image recognition. In The International Conference on Computer Vision and Pattern Recognition (CVPR’16). IEEE, 770–778. https://doi.org/10.1109/CVPR.2016.90Google ScholarCross Ref
- [131] . 2019. Auto-GNN: Neural architecture search of graph neural networks. arXiv (2019). https://arxiv.org/abs/1909.03184.Google Scholar
- [132] . 2020. Graph kernels: State-of-the-art and future challenges. Foundations and Trends in Machine Learning 13, 5–6 (2020), 531–712. https://doi.org/10.1561/2200000076Google ScholarDigital Library
- [133] . 2019. Fisher-Bures adversary graph convolutional networks. In The International Conference on Uncertainty in Artificial Intelligence (UAI’19). PMLR, No. 161.Google Scholar
- [134] . 2018. Structural deep embedding for hyper-networks. In The International Conference on Artificial Intelligence (AAAI’18). Association for the Advances of Artificial Intelligence, 426–433. Google ScholarDigital Library
- [135] . 2018. Deep recursive network embedding with regular equivalence. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 2357–2366. https://doi.org/10.1145/3219819.3220068 Google ScholarDigital Library
- [136] . 2018. Representation learning on graphs with jumping knowledge networks. In The International Conference on Machine Learning (ICML’18), Vol. 80. PMLR, 5453–5462.Google Scholar
- [137] . 2020. What can neural networks reason about. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [138] . 2019. How powerful are graph neural networks. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [139] . 2018. Inference in probabilistic graphical models by graph neural networks. In The International Conference on Learning Representations (ICLR’18). Vancouver, Canada.Google Scholar
- [140] . 2009. Feature hashing for large scale multitask learning. In The International Conference on Machine Learning (ICML’09). PMLR, 1113–1120. https://doi.org/10.1145/1553374.1553516 Google ScholarDigital Library
- [141] . 2018. Attention-based graph neural network for semi-supervised learning. arXiv (2018). https://arxiv.org/abs/1803.03735.Google Scholar
- [142] . 2018. Graph2Seq: Graph to sequence learning with attention-based neural networks. arXiv (2018). https://arxiv.org/abs/1804.00823.Google Scholar
- [143] . 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’14). ACL, 1724–1734. https://doi.org/10.3115/v1/D14-1179Google ScholarCross Ref
- [144] . 2020. Distance encoding: Design provably more powerful neural networks for graph representation learning. In The International Conference on Neural Information Processing Systems (NeurPS’20), Vol. 33. Curran Associates, Inc., 4465–4478.Google Scholar
- [145] . 2020. Adversarial attack and defense on graph data: A survey. arXiv (2020). https://arxiv.org/abs/1812.10528.Google Scholar
- [146] . 2019. Dynamically fused graph network for multi-hop reasoning. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 6140–6150.Google Scholar
- [147] . 2019. Amortized variational inference with graph convolutional networks for gaussian processes. In The International Conference on Artificial Intelligence and Statistics (AISTATS’19), Vol. 89. Society for Artificial Intelligence and Statistics, 2291–2300.Google Scholar
- [148] . 2018. Towards efficient large-scale graph neural network computing. arXiv (2018). https://arxiv.org/abs/1810.08403.Google Scholar
- [149] . 2020. PairNorm: Tackling oversmoothing in GNNs. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [150] . 2019. Relation-aware graph attention network for visual question answering. In The International Conference on Computer Vision (ICCV’19). IEEE, 10313–10322.Google ScholarCross Ref
- [151] . 2019. Point Cloud over Segmentation with graph-structured deep metric learning. In The International Conference on Computer Vision and Pattern Recognition (CVPR’19). IEEE, 7440–7449.Google Scholar
- [152] . 2019. Variational spectral graph convolutional networks. arXiv (2019). https://arxiv.org/abs/1906.01852v1.Google Scholar
- [153] . 2019. Gated graph convolutional recurrent neural networks. In The European Signal Processing Conference (EUSIPCO’19). IEEE, 1–5. https://doi.org/10.23919/EUSIPCO.2019.8902995Google ScholarCross Ref
- [154] . 2019. Learing to solve NP-Complete problems: A graph neural network for decision TSP. In The Conference on Artificial Intelligence (AAAI’19). Association for the Advances of Artificial Intelligence, 4731–4738. Google ScholarDigital Library
- [155] . 2018. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34, 13 (2018), i457–i466. https://doi.org/10.1093/bioinformatics/bty294Google ScholarCross Ref
- [156] . 2017. Dynamic edge-conditioned filters in convolutional neural networks on graphs. In The Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 29–38. https://doi.org/10.1109/CVPR.2017.11Google ScholarCross Ref
- [157] . 2018. GraphVAE: Towards generation of small graphs using variational autoencoders. In The International Conference on Artificial Neural Networks (ICANN’18), Vol. 11139. 412–422. https://doi.org/10.1007/978-3-030-01418-6_41Google ScholarCross Ref
- [158] . 2016. Learning convolutional neural networks for graphs. In The International Conference on Machine Learning (ICLR’16), Vol. 48. 2014–2023. Google ScholarDigital Library
- [159] . 2019. Fast graph representation learning with PyTorch Geometric. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [160] . 2018. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In The International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates, Inc., 2654–2665. Google ScholarDigital Library
- [161] . 2021. Interpreting and unifying graph neural networks with an optimization framework. In The World Wide Web Conference (WWW’21), Vol. 80. 1215–1226. Google ScholarDigital Library
- [162] . 2020. Towards deeper graph neural networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’20). 417, 338–348. https://doi.org/10.1145/3394486.3403076Google ScholarDigital Library
- [163] . 2019. GMNN: Graph markov neural networks. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 5241–5250.Google Scholar
- [164] . 2020. Rethinking pooling in graph neural networks. In The International Conference Neural Information Processing Systems (NeurPS’20), Vol. 33. Curran Associates, Inc., 2220–2231.Google Scholar
- [165] . 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In The International Conference on Neural Information Processing Systems (NIPS’16), Curran Associates, Inc.3844–3852. Google ScholarDigital Library
- [166] . 2017. Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine 34, 4 (2017), 18–42.Google ScholarCross Ref
- [167] . 2015. Deep convolutional networks on graph-structured data. arXiv (2015). https://arxiv.org/abs/1506.05163.Google Scholar
- [168] . 2018. Learning to represent programs with graphs. In The International Conference on Learning Representations (ICLR’18).Google Scholar
- [169] . 2019. Cognitive graph for multi-hop reading comprehension at scale. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 2694–2703.Google Scholar
- [170] . 2019. Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 2704–2713.Google Scholar
- [171] . 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. In The International Conference on Learning Representations (ICLR Workshop’19).Google Scholar
- [172] . 2018. Link prediction based on graph neural networks. In The International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates, Inc., 5171–5181. Google ScholarDigital Library
- [173] . 2018. An end-to-end deep learning architecture for graph classification. In The Conference on Artificial Intelligence (AAAI’18). Association for the Advances of Artificial Intelligence, 4438–4445. Google ScholarDigital Library
- [174] . 2019. HyperGCN: A new method for training graph convolutional networks on hypergraphs. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 1511–1522. Google ScholarDigital Library
- [175] . 2021. Graph neural networks for soft semi-supervised learning on hypergraphs. In The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’21). Springer, 447–458. https://doi.org/10.1007/978-3-030-75762-5_36Google ScholarDigital Library
- [176] . 2017. Visual interaction networks: Learning a physics simulator from video. In The International Conference on Neural Inforamtion Processing Systems (NIPS’17). Curran Associates, Inc., 4539–4547. Google ScholarDigital Library
- [177] . 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv (2018). https://arxiv.org/abs/1805.11973.Google Scholar
- [178] . 2019. Question answering by reasoning across documents with graph convolutional networks. In The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’19). ACL, 2306–2317.Google Scholar
- [179] . 2019. Universal invariant and equivariant graph neural networks. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 7092–7101. Google ScholarDigital Library
- [180] . 2018. Pre-training graph neural networks with kernels. arXiv (2018). https://arxiv.org/abs/1811.06930v1.Google Scholar
- [181] . 2019. Understanding the representation power of graph neural networks in learning graph topology. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 15413–15423. Google ScholarDigital Library
- [182] . 2019. Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’19). ACL, 3016–3025.Google ScholarCross Ref
- [183] . 2015. U-Net: Convolutional networks for biomedical image segmentation. In The International Conference on Medical Image Computing and Computer-assisted Intervention (MCCAI’15). 234–241.Google ScholarCross Ref
- [184] . 2019. Pitfalls of graph neural network evaluation. arXiv (2019). https://arxiv.org/abs/1811.05868.Google Scholar
- [185] . 2020. VSGNet: Spatial attention network for detecting human object interactions using graph convolutions. In The International Conference on Computer Vision and Pattern Recognition (CVPR’20). IEEE.Google ScholarCross Ref
- [186] . 2020. The logical expressiveness of graph neural networks. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [187] . 2019. Function space pooling for graph convolutional networks. arXiv (2019). https://arxiv.org/abs/1905.06259.Google Scholar
- [188] . 1994. Spectral K-way ratio-cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 13, 9 (1994), 1088–1096. https://doi.org/10.1109/43.310898 Google ScholarDigital Library
- [189] . 2019. Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case. arXiv (2019). https://arxiv.org/abs/1910.07421.Google Scholar
- [190] . 2019. GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 705–713. Google ScholarDigital Library
- [191] . 2018. Graph attention networks. In The International Conference on Learning Representations (ICLR’18).Google Scholar
- [192] . 2019. Deep graph infomax. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [193] . 2016. Interaction networks for learning about objects, relations and physics. In The International Conference on Neural Information Processing Systems (NIPS’16). Curran Associates, Inc., 4502–4510. Google ScholarDigital Library
- [194] . 2019. PiNet: A permutation invariant graph neural network for graph classification. arXiv (2019). https://arxiv.org/abs/1905.03046.Google Scholar
- [195] . 2018. Relational inductive biases, deep learning, and graph networks. arXiv (2018). https://arxiv.org/abs/1806.01261.Google Scholar
- [196] . 2019. Explainability methods for graph convolutional networks. In The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’19). IEEE, 10772–10781.Google ScholarCross Ref
- [197] . 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In The Conference on Artificial Intelligence (AAAI’18). Association for the Advances of Artificial Intelligence, 3538–3545. Google ScholarDigital Library
- [198] . 1998. A view of the EM algorithm that justifies incremental, sparse and other variants. Learning in Graphical Models 89 (1998), 355–368. https://doi.org/10.1007/978-94-011-5014-9_12 Google ScholarDigital Library
- [199] . 2020. Cross-modality attention with semantic graph embedding for multi-label classification. In The Conference on Artificial Intelligence (AAAI’20). Association for the Advances of Artificial Intelligence.Google ScholarCross Ref
- [200] . 2018. Graph partition neural networks for semi-supervised classification. In The International Conference on Learning Representations Workshop.Google Scholar
- [201] . 2019. LanczosNet: Multi-scale deep graph convolutional networks. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [202] . 2018. Hierarchical graph representation learning with differentiable pooling. In The International Conference on Neural Information Processing Systems (NeurPS’18). Curran Associates, Inc., 4805–4815. Google ScholarDigital Library
- [203] . 2014. Spherical and hyperbolic embeddings of data. IEEE Transactions on Pattern Recognition and Machine Intelligence 36, 11 (2014), 2255–2269. https://doi.org/10.1109/TPAMI.2014.2316836Google ScholarCross Ref
- [204] . 2019. Text generation from knowledge graphs with graph transformers. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’19), Vol. 1. ACL, 2284–2293. https://doi.org/10.18653/v1/N19-1238Google Scholar
- [205] . 2018. Covariant compositional networks for learning graphs. In The International Conference on Learning Representations Workshop.Google Scholar
- [206] . 2019. CayleyNets: Graph convolutional neural networks with complex relational spectral filters. IEEE Transactions on Signal Processing 67, 1 (2019), 97–109.Google ScholarDigital Library
- [207] . 2020. Hybrid spatio-temporal graph convolutional networks: Improving traffic prediction with navigation data. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’20). ACM, 3074–3082. https://doi.org/10.1145/3394486.3403358Google ScholarDigital Library
- [208] . 2018. Adaptive graph convolutional neural networks. In The Conference on Artificial Intelligence (AAAI’18). Associations for the Advances of Artificial Intelligence, 3546–3553. https://doi.org/10.1145/3292500.3330925 Google ScholarDigital Library
- [209] . 2019. Relational pooling for graph representations. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 4663–4673.Google Scholar
- [210] . 2020. A survey on the expressive power of graph neural networks. arXiv (2020). https://arxiv.org/abs/2003.04078.Google Scholar
- [211] . 2019. Approximation ratios of graph neural networks for combinatorial problems. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 4081–4090. Google ScholarDigital Library
- [212] . 2019. N-GCN: Multi-scale graph convolution for semi-supervised node classification. In The Conference on Uncertainty in Artificial Intelligence (UAI’19). PMLR, No. 310.Google Scholar
- [213] . 2019. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In The International Conference on Machine Learning (ICML’19), Vol. 97. PMLR, 21–29.Google Scholar
- [214] . 2017. Dynamic routing between capsules. In The International Conference on Neural Information Processing Systems (NIPS’17), Vol. 30. Curran Associates, Inc., 3856–3866. Google ScholarDigital Library
- [215] . 2018. Graph capsule convolutional neural networks. arXiv (2018). https://arxiv.org/abs/1805.08090.Google Scholar
- [216] . 2020. Joint embedding of structure and features via graph convolutional networks. Applied Network Science 5, 2020 (2020), No. 5. https://doi.org/10.1007/s41109-019-0237-xGoogle ScholarCross Ref
- [217] . 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 Google ScholarDigital Library
- [218] . 2019. Metapath-guided heterogeneous graph neural network for intent recommendation. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 2478–2486. https://doi.org/10.1145/3292500.3330673 Google ScholarDigital Library
- [219] . 2016. Deep neural networks for learning graph representations. In The Conference on Artificial Intelligence (AAAI’16). Association for the Advances of Artificial Intelligence, 1145–1152. Google ScholarDigital Library
- [220] . 2020. Transfer active learning for graph neural networks. OpenReview (2020). https://openreview.net/forum?id=BklOXeBFDS.Google Scholar
- [221] . 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In The International Conference on Artificial Intelligence (AAAI’19). Association for the Advances of Artificial Intelligence, 922–929. https://doi.org/10.1609/aaai.v33i01.3301922 Google ScholarDigital Library
- [222] . 2021. Motif-driven contrastive learning of graph representations. arXiv (2021). https://arxiv.org/abs/2012.12533v2.Google Scholar
- [223] . 2018. Adversarially regularized graph autoencoder for graph embedding. In The International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI Organization, 2609–2615. Google ScholarDigital Library
- [224] . 2020. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In The International Conference on Computer Vision and Pattern Recognition (CVPR’20). IEEE.Google ScholarCross Ref
- [225] . 2019. Session-based recommendation with graph neural networks. In The Conference on Artificial Intelligence (AAAI’19). Association for the Advances of Artificial Intelligence, 346–353. Google ScholarDigital Library
- [226] . 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. In The Conference on Artificial Intelligence (AAAI’18). Association for the Advances of Artificial Intelligence, 7444–7452. Google ScholarDigital Library
- [227] . 2017. Distance metric learning using graph convolutional networks: Application to functional brain networks. In The International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’17). 469–477.Google ScholarCross Ref
- [228] . 2020. RoadTagger: Robust road attribute inference with graph neural networks. In The Conference on Artificial Intelligence (AAAI’20). Association for the Advances of Artificial Intelligence.Google ScholarCross Ref
- [229] . 2016. Molecular graph convolutions: Moving beyond fingerprints. Journal of Computer-Aided Molecular Design 30, 8 (2016), 595–608. https://doi.org/10.1007/s10822-016-9938-8Google ScholarCross Ref
- [230] . 2018. Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification. In The International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI Organization, 3527–3534. https://doi.org/10.24963/ijcai.2018/490 Google ScholarDigital Library
- [231] . 2019. A lexicon-based graph neural network for Chinese NER. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’19). ACL, 1040–1050.Google ScholarCross Ref
- [232] . 2019. CGNF: Conditional graph neural fields. OpenReview (2019). https://openreview.net/forum?id=ryxMX2R9YQ.Google Scholar
- [233] . 2018. Graph convolutional networks with argument-aware pooling for event detection. In The Conference on Artificial Intelligence (AAAI’18). Association for the Advances of Artificial Intelligence, 5900–5907. Google ScholarDigital Library
- [234] . 2019. Neural architecture search: A survey. Journal of Machine Learning Research 20 (2019), 1–21. https://doi.org/10.1002/j.1538-7305.1970.tb01770.x Google ScholarDigital Library
- [235] . 2020. Contrastive learning of structured world models. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [236] . 2016. Variational graph auto-encoders. arXiv (2016). https://arxiv.org/abs/1611.07308.Google Scholar
- [237] . 2017. Semi-supervised classification with graph convolutional networks. In The International Conference on Learning Representations (ICLR’17).Google Scholar
- [238] . 2020. Spectral pyramid graph attention network for hyperspectral image classification. arXiv (2020). https://arxiv.org/abs/2001.07108.Google Scholar
- [239] . 2018. NerveNet: Learning structured policy with graph neural networks. In The International Conference on Learning Representations (ICLR’18).Google Scholar
- [240] . 2017. Column networks for collective classification. In The Conference on Artificial Intelligence (AAAI’17). Association for the Advances of Artificial Intelligence, 2485–2491. Google ScholarDigital Library
- [241] . 2019. GraphRel: Modeling text as relational graphs for joint entity and relation extraction. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 1409–1418.Google Scholar
- [242] . 2018. Signed graph convolutional network. In The International Conference on Data Mining (ICDM’18). IEEE, 929–934. https://doi.org/10.1109/ICDM.2018.00113Google ScholarCross Ref
- [243] . 2020. On the bottleneck of graph neural networks and its practical implications. arXiv (2020). https://arxiv.org/abs/2006.05205v2.Google Scholar
- [244] . 2018. Conversation modeling on Reddit using a graph-structured LSTM. Transactions of the Association for Computational Linguistics 6 (2018), 121–132. https://doi.org/10.1162/tacl_a_00009Google ScholarCross Ref
- [245] . 2020. Benchmarking graph neural networks. arXiv (2020). https://arxiv.org/abs/2003.00982.Google Scholar
- [246] . 2019. GraphMix: Regularized training of graph neural networks for semi-supervised learning. arXiv(2020), https://arxiv.org/abs/1909.11715v1.Google Scholar
- [247] . 2006. A comparison between recursive neural networks and graph neural networks. In The International Joint Conference on Neural Networks (IJCNN’06). IEEE, 778–785. https://doi.org/10.1109/IJCNN.2006.246763Google Scholar
- [248] . 2014. Recurrent models of visual attention. In The International Conference on Neural Information Processing Systems (NIPS’14), Vol. 2. Curran Associates, Inc., 2204–2212. Google ScholarDigital Library
- [249] . 2019. Coherent comments generation for Chinese articles with a graph-to-sequence model. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, Florence, Italy, 4843–4852.Google Scholar
- [250] . 2009. A game theoretical model for adversarial learning. In The International Conference on Data Mining Workshops (ICDM Workshop’09). IEEE, 25–30. Google ScholarDigital Library
- [251] . 2020. Strategies for pre-training graph neural networks. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [252] . 2019. Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 257–266. Google ScholarDigital Library
- [253] . 2018. Adaptive sampling towards fast graph representation learning. In The International Conference on Neural Information Processing Systems (NeurPS’18). Curran Associates, Inc., 4563–4572. Google ScholarDigital Library
- [254] . 2018. Learning deep network representations with adversarially regularized autoencoders. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’18). ACM, 2663–2671. https://doi.org/10.1145/3219819.3220000 Google ScholarDigital Library
- [255] . 2018. Junction tree variational autoencoder for molecular graph generation. In The International Conference on Machine Learning (ICML’18), Vol. 80. PMLR, 2323–2332.Google Scholar
- [256] . 2020. Deep learning for learning graph representations. Deep Learning: Concepts and Architectures 866 (2020), 169–210. https://doi.org/10.1007/978-3-030-31756-0_6Google ScholarCross Ref
- [257] . 2017. Inductive representation learning on large graphs. In The International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates, Inc., 1024–1034. Google ScholarDigital Library
- [258] . 2019. Attention, learn to solve routing problems. In The International Conference on Learning Representations (ICLR’19). https://openreview.net/forum?id=ByxBFsRqYm.Google Scholar
- [259] . 2019. KGAT: Knowledge graph attention network for recommendation. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 950–958. Google ScholarDigital Library
- [260] . 2019. Graph recurrent networks with attributed random walks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 732–740. https://doi.org/10.1145/3292500.3330941 Google ScholarDigital Library
- [261] . 2018. Jointly multiple events extraction via attention-based graph information aggregation. In The Conference on Empirical Methods in Natural Language Processing (EMNLP’18). ACL, 1247–1256.Google ScholarCross Ref
- [262] . 2020. Deep network embedding for graph representation learning in signed networks. IEEE Transactions on Cybernetics 50, 4 (2020), 1556–1568. https://doi.org/10.1109/TCYB.2018.2871503Google ScholarCross Ref
- [263] . 2018. Heterogeneous graph attention network. In The World Wide Web Conference (WWW’18). 2022–2032. https://doi.org/10.1145/3308558.3313562 Google ScholarDigital Library
- [264] . 2020. Multi-component graph convolutional collaborative filtering. In The Conference on Artificial Intelligence (AAAI’20). Association for the Advances of Artificial Intelligence.Google ScholarCross Ref
- [265] . 2017. Interpretable structure-evolving LSTM. In The International Conference on Computer Vision and Pattern Recognition (CVPR’17). IEEE, 1010–1019.Google ScholarCross Ref
- [266] . 2016. Semantic object parsing with graph LSTM. In The European Conference on Computer Vision (ECCV’16). Springer, 125–143.Google ScholarCross Ref
- [267] . 2018. Deep graph translation. arXiv (2018). https://arxiv.org/abs/1805.09980.Google Scholar
- [268] . 2018. Non-local neural networks. In The International Conference on Computer Vision and Pattern Recognition (CVPR’18). IEEE, 7794–7803.Google ScholarCross Ref
- [269] . 2020. Dynamically pruned message passing networks for large-scale knowledge graph reasoning. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [270] . 2020. Tensor graph convolutional networks for text classification. arXiv (2020). https://arxiv.org/abs/2001.05313.Google Scholar
- [271] . 2019. Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks. OpenReview (2019). https://openreview.net/forum?id=rJl05AVtwB.Google Scholar
- [272] . 2019. Capsule graph neural network. In The International Conference on Learning Representations (ICLR’19).Google Scholar
- [273] . 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences 63 (2020), 1872–1897. https://doi.org/10.1007/s11431-020-1647-3Google ScholarCross Ref
- [274] . 2019. Conditional structure generation through graph variational generative adversarial nets. In The International Conference on Neural Information Processing Systems (NeurPS’19), Vol. 32. Curran Associates, Inc. Google ScholarDigital Library
- [275] . 2009. Gradient-based learning applied to document recognition. The IEEE 86, 11 (2009), 22778–2324. https://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdf.Google ScholarCross Ref
- [276] . 2020. CAGNN: Cluster-aware graph neural networks for unsupervised graph representation learning. arXiv (2020). https://arxiv.org/abs/2009.01674.Google Scholar
- [277] . 2019. Multi-dimensional graph convolutional networks. In The SIAM International Conference on Data Mining (SDM’19). SIAM, 657–665. https://doi.org/10.1137/1.9781611975673.74Google ScholarCross Ref
- [278] . 2019. Graph convolutional networks with EigenPooling. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’19). ACM, 723–731. https://doi.org/10.1145/3292500.3330982 Google ScholarDigital Library
- [279] . 2020. Streaming graph neural networks. In The International Conference on Research and Development in Information Retrieval (SIGIR’20). ACM, Virtual Event, 719–728. https://doi.org/10.1145/3397271.3401092 Google ScholarDigital Library
- [280] . 2021. Self-supervised learning of graph neural networks: A unified review. arXiv (2021). https://arxiv.org/abs/2102.10757v2.Google Scholar
- [281] . 2020. Every node counts: Self-ensembling graph convolutional networks for semi-supervised learning. Pattern Recognition 106 (2020), No. 107451. https://doi.org/10.1016/j.patcog.2020.107451Google ScholarCross Ref
- [282] . 2017. VAIN: Attentional multi-agent predictive modeling. In The International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates, Inc., 2698–2708. Google ScholarDigital Library
- [283] . 2018. M-Walk: Learning to walk over graphs using monte carlo tree search. In The International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates, Inc., 6786–6797. Google ScholarDigital Library
- [284] . 2019. Hypergraph neural networks. In The Conference on Artificial Intelligence (AAAI’19). Association for the Advances of Artificial Intelligence, 3558–3565. https://doi.org/10.1609/aaai.v33i01.33013558 Google ScholarDigital Library
- [285] . 2020. HNHN: Hypergraph networks with hyperedge neurons. arXiv (2020). https://arxiv.org/abs/2006.12278.Google Scholar
- [286] . 2018. Factorizable Net: An efficient subgraph-based framework for scene graph generation. In The European Conference on Computer Vision (ECCV’18). Springer, Munich, Germany.Google ScholarCross Ref
- [287] . 2019. MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In The International Conference on Multimedia (MM’19). ACM, 1437–1445. Google ScholarDigital Library
- [288] . 2020. When do GNNs work: Understanding and improving neighborhood aggregation. In The International Joint Conference on Artificial Intelligence (IJCAI’20). IJCAI Organization, 1303–1309. https://doi.org/10.24963/ijcai.2020/181 Google ScholarDigital Library
- [289] . 2021. Graph self-supervised learning: A survey. arXiv (2021). https://arxiv.org/abs/2103.00111v1.Google Scholar
- [290] . 2018. Deep collective classification in heterogeneous information networks. In The World Wide Web Conference (WWW’18). 399–408. https://doi.org/10.1145/3178876.3186106 Google ScholarDigital Library
- [291] . 2020. Learning cross-modal context graph for visual grounding. In The Conference on Artificial Intelligence (AAAI’20). Association for the Advances of Artificial Intelligence.Google ScholarCross Ref
- [292] . 2020. Graph contrastive learning with augmentations. In The International Conference on Neural Information Processing Systems (NeurPS’20), Vol. 33. Curran Associates, Inc., 5812–5823.Google Scholar
- [293] . 2018. Structured sequence modeling with graph convolutional recurrent networks. In The International Conference on Neural Information Processing (ICONIP’18), Vol. 11301. 362–373. https://doi.org/10.1007/978-3-030-04167-0_33Google ScholarDigital Library
- [294] . 2019. BAG: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. In The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’19). ACL, 357–362.Google Scholar
- [295] . 2020. Reinforcement learning based graph-to-sequence model for natural question generation. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [296] . 2018. Learning graph-level representations with recurrent neural networks. arXiv (2018). https://arxiv.org/abs/1805.07683.Google Scholar
- [297] . 2020. DropEdge: Towards deep graph convolutional networks on node classification. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [298] . 2019. Recurrent meta-structure for robust similarity measure in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data 13, 6 (2019), No. 64. https://doi.org/10.1145/3364226 Google ScholarDigital Library
- [299] . 2019. Graph transformer. OpenReview (2019). https://openreview.net/forum?id=HJei-2RcK7.Google Scholar
- [300] . 2021. Learning to pre-train graph neural networks. In The International Conference on Artificial Intelligence (AAAI’21). Association for the Advances of Artificial Intelligence.Google Scholar
- [301] . 2019. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics 38, 5 (2019), No. 146. https://doi.org/10.1145/3326362 Google ScholarDigital Library
- [302] . 2018. Sentence-State LSTM for text representation. In The Annual Meeting of the Association for Computational Linguistics (ACL’18), Vol. 1. ACL, 317–327. https://doi.org/10.18653/v1/P18-1030Google Scholar
- [303] . 2016. Gated graph sequence neural networks. In The International Conference on Learning Representations (ICLR’16). Caribe Hilton.Google Scholar
- [304] . 2018. Learning deep generative models of graphs. In The International Conference on Learning Representations Workshop.Google Scholar
- [305] . 2019. Exploiting spatial-temporal relationships for 3d pose estimation via graph convolutional networks. In The International Conference on Computer Vision (ICCV’19). IEEE, 2272–2281.Google ScholarCross Ref
- [306] . 2019. Graph transformer networks. In The International Conference on Neural Information Processing Systems (NeurPS’19), Vol. 32. Curran Associates, Inc., 1–11. Google ScholarDigital Library
- [307] . 2019. SimGNN: A neural network approach to fast graph similarity computation. In The International Conference on Web Search and Data Mining (WSDM’19). ACM, 384–392. Google ScholarDigital Library
- [308] . 2020. Efficient probabilistic logic reasoning with graph neural networks. In The International Conference on Learning Representations (ICLR’20). Addis Ababa.Google Scholar
- [309] . 2020. Fractional graph convolutional networks (FGCN) for semi-supervised learning. (2020). https://openreview.net/forum?id=BygacxrFwS.Google Scholar
- [310] . 2019. D-VAE: A variational autoencoder for directed acyclic graphs. In The International Conference on Neural Information Processing Systems (NeurPS’19), Vol. 32. Curran Associates, Inc. Google ScholarDigital Library
- [311] . 2020. Graph representation learning via graphical mutual information maximization. In The World Wide Web Conference (WWW’20). 259–270. Google ScholarDigital Library
- [312] . 2018. ANRL: Attributed network representation learning via deep neural networks. In The International Joint Conference on Artificial Intelligence (IJCAI’18). IJCAI Organization, 3155–3161. https://doi.org/10.24963/ijcai.2018/438 Google ScholarDigital Library
- [313] . 2019. Quantum-based subgraph convolutional neural networks. Pattern Recognition 88, 2019 (2019), 38–49. https://doi.org/10.1016/j.patcog.2018.11.002Google ScholarCross Ref
- [314] . 2019. Depth-based subgraph convolutional auto-encoder for network representation learning. Pattern Recognition 90 (2019), 363–376. https://doi.org/10.1016/j.patcog.2019.01.045Google ScholarDigital Library
- [315] . 2019. Attention guided graph convolutional networks for relation extraction. In The Annual Meeting of the Association for Computational Linguistics (ACL’19). ACL, 241–251.Google Scholar
- [316] . 2019. Batch virtual adversarial training for graph convolutional networks. In The International Conference on Machine Learning Workshop. PMLR.Google Scholar
- [317] . 2019. GNNExplainer: Generating explanations for graph neural networks. In The International Conference on Neural Information Processing Systems (NeurPS’19). Curran Associates, Inc., 9244–9255. Google ScholarDigital Library
- [318] . 2021. Graph contrastive learning with adaptive augmentation. In The World Wide Web Conference (WWW’21). Ljubljana, 1. Google ScholarDigital Library
- [319] . 2018. Combinatorial optimization with graph convolutional networks and guided tree search. In The International Conference on Neural Information Processing Systems (NIPS’18). Curran Associates, Inc., 573–546. Google ScholarDigital Library
- [320] . 2019. Pre-training graph neural networks for generic structural feature extraction. arXiv (2019). https://arxiv.org/abs/1905.13728.Google Scholar
- [321] . 2019. Unsupervised pre-training of graph convolutional networks. In The International Conference on Learning Representations Workshop. https://arxiv.org/abs/1905.13728.Google Scholar
- [322] . 2020. GPT-GNN: Generative pre-training of graph neural networks. In The International Conference on Knowledge Discovery and Data Mining (SIGKDD’20). ACM, 1157–1167. https://doi.org/10.1145/3394486.3403237Google ScholarDigital Library
- [323] . 2020. Heterogeneous graph transformer. In The World Wide Web Conference (WWW’20). Taipei, 2704–2710. https://doi.org/10.1145/3366423.3380027 Google ScholarDigital Library
- [324] . 2019. GeniePath: Graph neural networks with adaptive receptive paths. In The Conference on Artificial Intelligence (AAAI’19). Association for the Advances of Artificial Intelligence, 4424–4431. Google ScholarDigital Library
- [325] . 2020. Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering 34, 1 (2020), 249–270. https://doi.org/10.1109/TKDE.2020.2981333Google Scholar
- [326] . 2021. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4–24.Google ScholarCross Ref
- [327] . 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. In The International Conference on Neural Information Processing Systems (NeurPS’19), Vol. 32. Curran Associates, Inc.Google Scholar
Index Terms
- Graph Neural Networks: Taxonomy, Advances, and Trends
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