A Bayesian Metric for Network Similarity

Abstract

Networks of every kind and in numerous fields are omnipresent in today’s society (e.g. brain networks, social networks) and are the intense subject of research. It would be of great utility to have a computationally efficient and generally applicable method for assessing similarity of metrics. The field (going back to the 1950s) has not come up with such a method (a few moves in this direction exist, such as Jaccard coefficients, QAP--quadratic assignment procedure, and more recently Menezes & Roth, 2013, and Asta & Shalizi, 2014). I present a Bayesian-based metric for assessing similarity of two networks, possibly of different size, that include nodes and links between nodes. I assume the nodes are labeled so that both the nodes and links between two nodes that are shared between the two networks can be identified.


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