DELTACON: A Principled Massive-Graph Similarity Function with Attribution

Koutra, Danai; Shah, Neil; Vogelstein, Joshua T.; Gallagher, Brian; Faloutsos, Christos

doi:10.1145/2824443

Title: DELTACON: A Principled Massive-Graph Similarity Function with Attribution

Journal Article · Thu May 22 00:00:00 EDT 2014 · ACM Transactions on Knowledge Discovery from Data

DOI:https://doi.org/10.1145/2824443· OSTI ID:1343040

Koutra, Danai ^[1]; Shah, Neil ^[1]; Vogelstein, Joshua T. ^[2]; Gallagher, Brian ^[3]; Faloutsos, Christos ^[1]

Carnegie Mellon Univ., Pittsburgh, PA (United States). Computer Science Dept.
Duke Univ., Durham, NC (United States). Dept. of Statistical Science
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1343040

Report Number(s):: LLNL-JRNL-677691

Journal Information:: ACM Transactions on Knowledge Discovery from Data, Vol. 10, Issue 3; Conference: SIAM International Conference on Data Mining, Austin, Texas, USA, 5/02/2013 - 5/04/2013; ISSN 1556-4681

Publisher:: Association for Computing MachineryCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 78 works

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99 GENERAL AND MISCELLANEOUS
97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
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experimentation
graph similarity
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network monitoring
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Title: DELTACON: A Principled Massive-Graph Similarity Function with Attribution

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