ABSTRACT
How can the most appropriate community be found given an attributed graph and a user-specified query node? The community search algorithm is currently an essential graph data management tool to find a community suited to a user-specified query node. Although community search algorithms are useful in various web-based applications and services, they have trouble handling attributed graphs due to the strict topological constraints of traditional algorithms. In this paper, we propose an accurate community search algorithm for attributed graphs. To overcome current limitations, we define a new attribute-driven community search problem class called the Flexible Attributed Truss Community (F-ATC). The advantage of the F-ATC problem is that it relaxes topological constraints, allowing diverse communities to be explored. Consequently, the community search accuracy is enhanced compared to traditional community search algorithms. Additionally, we present a novel heuristic algorithm to solve the F-ATC problem. This effective algorithm detects more accurate communities from attributed graphs than the traditional algorithms. Finally, extensive experiments are conducted using real-world attributed graphs to demonstrate that our approach achieves a higher accuracy than the state-of-the-art method.
- Jonathan D. Cohen. 2008. Trusses: Cohesive Subgraphs for Social Network Analysis. National Security Agency (2008).Google Scholar
- Javier O. Garcia, Arian Ashourvan, Sarah Muldoon, Jean M. Vettel, and Danielle S. Bassett. 2018. Applications of Community Detection Techniques to Brain Graphs: Algorithmic Considerations and Implications for Neural Function. Proc. IEEE 106, 5 (May 2018), 846--867. https://doi.org/10.1109/JPROC.2017.2786710Google Scholar
- Chenjuan Guo, Bin Yang, Jilin Hu, and Christian S.Jensen. 2018. Learning to Route with Sparse Trajectory Sets. In the 34th IEEE International Conference on Data Engineering (ICDE).Google Scholar
- Xin Huang, Hong Cheng, Lu Qin, Wentao Tian, and Jeffrey Xu Yu. 2014. Querying K-truss Community in Large and Dynamic Graphs. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD 2014). 1311--1322.Google ScholarDigital Library
- Xin Huang and Laks Lakshmanan. 2017. Attribute-Driven Community Search. Proceedings of the VLDB Endowment 10, 9 (2017), 949--960.Google ScholarDigital Library
- Xin Huang, Laks V. S. Lakshmanan, Jeffrey Xu Yu, and Hong Cheng. 2015. Approximate Closest Community Search in Networks. Proceedings of the VLDB Endowment 9, 4 (December 2015), 276--287. arXiv:1505.05956Google ScholarDigital Library
- George Karypis and Vipin Kumar. 1998. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM Journal on Scientific Computing 20, 1 (December 1998), 359--392.Google ScholarDigital Library
- Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2007. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (ACM TKDD) 1, 1, Article 2 (March 2007).Google ScholarDigital Library
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.Google Scholar
- Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.Google Scholar
- Julian McAuley and Jure Leskovec. 2012. Learning to Discover Social Circles in Ego Networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS 2012). 539--547.Google ScholarDigital Library
- Makoto Onizuka, Toshimasa Fujimori, and Hiroaki Shiokawa. 2017. Graph Partitioning for Distributed Graph Processing. Data Science and Engineering 2, 1 (01 Mar 2017), 94--105.Google Scholar
- Tomoki Sato, Hiroaki Shiokawa, Yuto Yamaguchi, and Hiroyuki Kitagawa. 2018. FORank: Fast ObjectRank for Large Heterogeneous Graphs. In Companion Proceedings of the The Web Conference 2018. 103--104.Google ScholarDigital Library
- Jianbo Shi and Jitendra Malik. 2000. Normalized Cuts and Image Segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 22, 8 (August 2000), 888--905.Google Scholar
- Hiroaki Shiokawa, Toshiyuki Amagasa, and Hiroyuki Kitagawa. 2019. Scaling Fine-grained Modularity Clustering for Massive Graphs. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI2019). 4597--4604.Google ScholarCross Ref
- Hiroaki Shiokawa, Yasuhiro Fujiwara, and Makoto Onizuka. 2013. Fast Algorithm for Modularity-Based Graph Clustering. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2013), 1170--1176.Google ScholarDigital Library
- Hiroaki Shiokawa, Yasuhiro Fujiwara, and Makoto Onizuka. 2015. SCAN++: Efficient Algorithm for Finding Clusters, Hubs and Outliers on Large-scale Graphs. Proceedings of Very Learge Data Bases 8, 11 (2015), 1178--1189.Google Scholar
- Hiroaki Shiokawa and Makoto Onizuka. 2017. Scalable Graph Clustering and Its Applications. Springer New York, New York, NY, 1--10.Google Scholar
- Hiroaki Shiokawa, Tomokatsu Takahashi, and Hiroyuki Kitagawa. 2018. ScaleSCAN: Scalable Density-based Graph Clustering. In Proceedings of the 29th International Conference on Database and Expert Systems Applications (DEXA). 18--34.Google ScholarDigital Library
- Mauro Sozio and Aristides Gionis. 2010. The Community-Search Problem and How to Plan a Successful Cocktail Party. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010). ACM, New York, NY, USA, 939--948.Google ScholarDigital Library
- Tomokatsu Takahashi, Hiroaki Shiokawa, and Hiroyuki Kitagawa. 2017. SCANXP: Parallel Structural Graph Clustering Algorithm on Intel Xeon Phi Coprocessors. In Proceedings of the 2nd International Workshop on Network Data Analytics (NDA). 6:1--6:7.Google ScholarDigital Library
- Xiao Zhang and M. E.J. Newman. 2015. Multiway Spectral Community Detection in Networks. Physical Review E 92 (Nov 2015), 052808. Issue 5.Google Scholar
- Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2009. Graph Clustering Based on Structural/Attribute Similarities. Proceedings of the VLDB Endowment 2, 1 (August 2009), 718--729.Google ScholarDigital Library
Index Terms
- Flexible Community Search Algorithm on Attributed Graphs
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