DELTACON: Principled Massive-Graph Similarity Function with Attribution

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 (similar to 17M edges, up to 90M edges) for 21 subjects.