DELTACON: A Principled Massive-Graph Similarity Function by Danai Koutra, Joshua T. Vogelstein, Christos Faloutsos.
Abstract:
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). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we 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.
How different is your current topic map from a prior version?
Could be an interesting marketing ploy to colorize the distinct portions of the graph.
Not to mention using “similarity” to mean the same subject for some purposes. Group subjects come to mind.
And for other types of analysis.