Optimal Laplacian regularization for sparse spectral community detection
arXiv:1912.01419v214 citations
AI Analysis
This work solves a specific problem in network analysis for researchers, but it is incremental as it builds on existing regularization methods.
The paper formally determines an optimal regularization for Laplacian matrices to improve spectral clustering in sparse networks, addressing a previously heuristic approach.
Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we formally determine a proper regularization which is intimately related to alternative state-of-the-art spectral techniques for sparse graphs.