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Network mutual information measures for graph similarity
Title / Series / Name
Publication Volume
Publication Issue
Pages
Editors
Keywords
General Physics and Astronomy
URI
https://hdl.handle.net/20.500.14018/26446
Abstract
A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2024-10-14
Language
ISBN
Identifiers
10.1038/s42005-024-01830-3