Distribution-Free Model for Community Detection
This work addresses the challenge of community detection in weighted networks, which is important for network analysis applications, but it appears incremental as it builds on existing stochastic blockmodels.
The paper tackles community detection in weighted networks by proposing a Distribution-Free Model (DFM) that generalizes stochastic blockmodels without requiring specific distribution assumptions, and shows through experiments that benchmark algorithms can successfully recover community membership in networks generated by this model.
Community detection for unweighted networks has been widely studied in network analysis, but the case of weighted networks remains a challenge. This paper proposes a general Distribution-Free Model (DFM) for weighted networks in which nodes are partitioned into different communities. DFM can be seen as a generalization of the famous stochastic blockmodels from unweighted networks to weighted networks. DFM does not require prior knowledge of a specific distribution for elements of the adjacency matrix but only the expected value. In particular, signed networks with latent community structures can be modeled by DFM. We build a theoretical guarantee to show that a simple spectral clustering algorithm stably yields consistent community detection under DFM. We also propose a four-step data generation process to generate adjacency matrices with missing edges by combining DFM, noise matrix, and a model for unweighted networks. Using experiments with simulated and real datasets, we show that some benchmark algorithms can successfully recover community membership for weighted networks generated by the proposed data generation process.