Big-Align: Fast bipartite graph alignment

Danai Koutra, Hanghang Tong, David Lubensky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

95 Scopus citations


How can we find the virtual twin (i.e., the same or similar user) on Linked In for a user on Facebook? How can we effectively link an information network with a social network to support cross-network search? Graph alignment - the task of finding the node correspondences between two given graphs - is a fundamental building block in numerous application domains, such as social networks analysis, bioinformatics, chemistry, pattern recognition. In this work, we focus on aligning bipartite graphs, a problem which has been largely ignored by the extensive existing work on graph matching, despite the ubiquity of those graphs (e.g., users-groups network). We introduce a new optimization formulation and propose an effective and fast algorithm to solve it. We also propose a fast generalization of our approach to align unipartite graphs. The extensive experimental evaluations show that our method outperforms the state-of-art graph matching algorithms in both alignment accuracy and running time, being up to 10× more accurate or 174× faster on real graphs.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Number of pages10
StatePublished - 2013
Externally publishedYes
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013


Other13th IEEE International Conference on Data Mining, ICDM 2013
Country/TerritoryUnited States
CityDallas, TX

ASJC Scopus subject areas

  • Engineering(all)


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