arXiv Machine Learning June 25, 2026 By Sie Hendrata Dharmawan, Peter Chin

Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection

Read the original on arXiv Machine Learning →

arXiv:2602. 17104v2 Announce Type: replace-cross Abstract: We propose a streamlined spectral algorithm for community detection in the two-community stochastic block model (SBM) under constant edge density assumptions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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arXiv:2608. 11321v1 Announce Type: cross Abstract: We study spectral clustering in the presence of a confounding latent geometry.

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Spectral Embeddings of Degree-$\alpha$ Laplacians in Random Dot Product Graphs

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rag

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arXiv Machine Learning

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Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling

arXiv:2608. 03696v1 Announce Type: new Abstract: This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dynamics - a task related to pooling in machine learning on graphs, or community detection in network science.

By Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore Trani

benchmarks

arXiv Machine Learning

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Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph

arXiv:2608. 03695v1 Announce Type: cross Abstract: This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs.

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Spectral graph clustering with inhomogeneous latent geometry

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