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Efficient computation of distance labeling for decremental updates in large dynamic graphs

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Abstract

Since today’s real-world graphs, such as social network graphs, are evolving all the time, it is of great importance to perform graph computations and analysis in these dynamic graphs. Due to the fact that many applications such as social network link analysis with the existence of inactive users need to handle failed links or nodes, decremental computation and maintenance for graphs is considered a challenging problem. Shortest path computation is one of the most fundamental operations for managing and analyzing large graphs. A number of indexing methods have been proposed to answer distance queries in static graphs. Unfortunately, there is little work on answering such queries for dynamic graphs. In this paper, we focus on the problem of computing the shortest path distance in dynamic graphs, particularly on decremental updates (i.e., edge deletions). We propose maintenance algorithms based on distance labeling, which can handle decremental updates efficiently. By exploiting properties of distance labeling in original graphs, we are able to efficiently maintain distance labeling for new graphs. We experimentally evaluate our algorithms using eleven real-world large graphs and confirm the effectiveness and efficiency of our approach. More specifically, our method can speed up index re-computation by up to an order of magnitude compared with the state-of-the-art method, Pruned Landmark Labeling (PLL).

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Notes

  1. http://wiki.dbpedia.org/

  2. Note that, this concept is a special case of hierarchical labeling proposed in 1

  3. Note that, we use a BFS manner to identify P A(0) and P A(8) in Algorithm 1 as we will show later that all vertices in P A(0) will appear consecutively in Lemma 5.

  4. https://github.com/iwiwi/pruned-landmark-labeling

  5. http://snap.stanford.edu/

  6. http://konect.uni-koblenz.de/networks/

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Authors and Affiliations

  1. School of Computing and Engineering, University of Huddersfield, Huddersfield, UK

    Yongrui Qin & Simon Parkinson

  2. School of Computer Science, The University of Adelaide, Adelaide, Australia

    Quan Z. Sheng & Nickolas J. G. Falkner

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia

    Lina Yao

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  1. Yongrui Qin
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  2. Quan Z. Sheng
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  3. Nickolas J. G. Falkner
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  4. Lina Yao
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  5. Simon Parkinson
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Correspondence to Yongrui Qin.

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Qin, Y., Sheng, Q.Z., Falkner, N.J.G. et al. Efficient computation of distance labeling for decremental updates in large dynamic graphs. World Wide Web 20, 915–937 (2017). https://doi.org/10.1007/s11280-016-0421-1

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