research-article

DeltaCon: Principled Massive-Graph Similarity Function with Attribution

Authors:

Joshua T. Vogelstein,

Brian Gallagher,

Christos FaloutsosAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 3

Article No.: 28, Pages 1 - 43

https://doi.org/10.1145/2824443

Published: 24 February 2016 Publication History

Abstract

How much has a network changed since yesterday? How different is the wiring of Bob’s brain (a left-handed male) and Alice’s brain (a right-handed female), and how is it different? Graph similarity with given 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 conjunction, we propose DeltaCon-Attr, a related approach that enables attribution of change or dissimilarity to responsible nodes and edges. Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon and DeltaCon-Attr on real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, (b) do temporal anomaly detection in the who-emails-whom Enron graph and find the top culprits for the changes in the temporal corporate email graph, and (c) recover pairs of test-retest large brain scans ( ∼17M edges, up to 90M edges) for 21 subjects.

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Index Terms

DeltaCon: Principled Massive-Graph Similarity Function with Attribution
1. Information systems
  1. Information systems applications

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 3

February 2016

358 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2888412

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Publication History

Published: 24 February 2016

Accepted: 01 September 2015

Revised: 01 August 2015

Received: 01 May 2014

Published in TKDD Volume 10, Issue 3

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U.S. Department of Energy by Lawrence Livermore National Laboratory
Yahoo Research Alliance Gift
U.S. Army Research Office (ARO) and Defense Advanced Research Projects Agency (DARPA)
IBM Faculty Award
Army Research Laboratory
Google Focused Research Award

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