HashAlign: Hash-Based Alignment of Multiple Graphs (original) (raw)

Abstract

Fusing or aligning two or more networks is a fundamental building block of many graph mining tasks (e.g., recommendation systems, link prediction, collective analysis of networks). Most past work has focused on formulating pairwise graph alignment as an optimization problem with varying constraints and relaxations. In this paper, we study the problem of multiple graph alignment (collectively aligning multiple graphs at once) and propose HashAlign, an efficient and intuitive hash-based framework for network alignment that leverages structural properties and other node and edge attributes (if available) simultaneously. We introduce a new construction of LSH families, as well as robust node and graph features that are tailored for this task. Our method quickly aligns multiple graphs while avoiding the all-pairwise-comparison problem by expressing all alignments in terms of a chosen ‘center’ graph. Our extensive experiments on synthetic and real networks show that, on average, HashAlign is \(2{\times }\) faster and 10 to 20% more accurate than the baselines in pairwise alignment, and \(2{\times }\) faster while 50% more accurate in multiple graph alignment.

M. Heimann and W. Lee—These authors contributed equally to this work.

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Acknowledgements

This material is based upon work supported in part by the National Science Foundation under Grant No. IIS 1743088, and the University of Michigan. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding parties. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Authors and Affiliations

  1. Computer Science and Engineering, University of Michigan, Ann Arbor, USA
    Mark Heimann, Wei Lee, Shengjie Pan, Kuan-Yu Chen & Danai Koutra

Authors

  1. Mark Heimann
  2. Wei Lee
  3. Shengjie Pan
  4. Kuan-Yu Chen
  5. Danai Koutra

Corresponding author

Correspondence toMark Heimann .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia
    Dinh Phung
  2. National Chiao Tung University, Hsinchu City, Taiwan
    Vincent S. Tseng
  3. Monash University, Clayton, Victoria, Australia
    Geoffrey I. Webb
  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
    Bao Ho
  5. University of Melbourne, Melbourne, Victoria, Australia
    Mohadeseh Ganji
  6. University of Melbourne, Melbourne, Victoria, Australia
    Lida Rashidi

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Heimann, M., Lee, W., Pan, S., Chen, KY., Koutra, D. (2018). HashAlign: Hash-Based Alignment of Multiple Graphs. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4\_57

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