Techniques for clustering interaction data as a collection of graphs
This work addresses the need for graph clustering in fields like neuroscience and social network analysis, offering an incremental improvement by combining existing methods into a new framework.
The paper tackles the problem of clustering interaction data represented as sequences of graphs by formulating it as a model selection problem, using techniques like information criteria, non-negative matrix factorization, and singular value thresholding, and demonstrates results on real and simulated data.
A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction). Such discretization together with non-negative factorization techniques can be useful for obtaining clustering of graphs. Motivating application of performing clustering of graphs (as opposed to vertex clustering) can be found in neuroscience and in social network analysis, and it can also be used to enhance community detection (i.e., vertex clustering) by way of conditioning on the cluster labels. In this paper, we formulate a problem of clustering of graphs as a model selection problem. Our approach involves information criteria, non-negative matrix factorization and singular value thresholding, and we illustrate our techniques using real and simulated data.