Bayesian Spiked Laplacian Graphs

In network analysis, it is common to work with a collection of graphs that exhibit heterogeneity. For example, neuroimaging data from patient cohorts are increasingly available. A critical analytical task is to identify communities, and graph Laplacian-based methods are routinely used. However, these methods are currently limited to a single network and also do not provide measures of uncertainty on the community assignment. In this work, we first propose a probabilistic network model called the ”Spiked Laplacian Graph” that considers …

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