MASO and GeoDe are proposed to recover communities in latent-geometry SBMs, and their empirical gains are not backed by guarantees that apply to the actual algorithms.
Non-backtracking spectrum of random graphs: community detection and non-regular Ramanujan graphs
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges and can be used to count non-backtracking walks of a given length. It has been used recently in the context of community detection and has appeared previously in connection with the Ihara zeta function and in some generalizations of Ramanujan graphs. In this work, we study the largest eigenvalues of the non-backtracking matrix of the Erdos-Renyi random graph and of the Stochastic Block Model in the regime where the number of edges is proportional to the number of vertices. Our results confirm the "spectral redemption" conjecture that community detection can be made on the basis of the leading eigenvectors above the feasibility threshold.
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cs.SI 1years
2025 1verdicts
REJECT 1representative citing papers
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Community Recovery on Noisy Stochastic Block Models
MASO and GeoDe are proposed to recover communities in latent-geometry SBMs, and their empirical gains are not backed by guarantees that apply to the actual algorithms.