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A Spectral Framework for Anomalous Subgraph Detection
Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraph's adjacency matrix (signal power) and of the background graph's residuals matrix (noise power).
Co-clustering separately exchangeable network data
This article establishes the performance of stochastic blockmodels in addressing the co-clustering problem of partitioning a binary array into subsets, assuming only that the data are generated by a nonparametric process satisfying the condition of separate exchangeability.
Confidence Sets for Network Structure
Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs.
Subgraph Detection Using Eigenvector L1 Norms
When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies.
