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A statistical test for network similarity
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DOI：10.1088/2632-072X/ae5d25.png)
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Abstract
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In this article, we revisit and expand our prior work on graph similarity. As with our earlier work, we focus on a view of similarity which does not require node correspondence between graphs under comparison. Our work is suited to the temporal study of networks, change-point and anomaly detection and simple comparisons of static graphs. It provides a similarity metric for the study of (weakly) connected graphs. Our work proposes a metric designed to compare networks and assess the (dis)similarity between them. For example, given three different graphs with possibly different numbers of nodes, G1, G2 and G3, we aim to answer two questions: a) ‘How different is G1 from G2?’ and b) ‘Is graph G3 more similar to G1 or to G2?’. We illustrate the value of our test and its accuracy through several new experiments, using synthetic and real-world graphs.
Keywords:
network similarity
graph comparison
statistical test
change-point detection
anomaly detection
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