arXiv Machine Learning
6d ago
Spectral graph clustering with inhomogeneous latent geometry
arXiv:2608. 11321v1 Announce Type: cross Abstract: We study spectral clustering in the presence of a confounding latent geometry.
Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
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.
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arXiv Machine Learning
Aug 12
Spectral Embeddings of Degree-$\alpha$ Laplacians in Random Dot Product Graphs
arXiv:2608. 10845v1 Announce Type: cross Abstract: Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the symmetric Laplacian.
arXiv Machine Learning
Jun 9
Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
arXiv:2606. 09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks.
arXiv AI
Jul 8
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
arXiv Machine Learning
Aug 5
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.
arXiv Machine Learning
Aug 5
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.
Hugging Face Trending Papers
Aug 4
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs. Built on the NVIDIA RAPIDS ecosystem, the framework enables the characterization and tracking of communities in snapshot-based dynamic graphs, either by Leiden greedy optimization with multi-GPU support via Dask-based workload distribution, or eigendecomposition of a symmetric Bethe-Hessian operator.
arXiv Machine Learning
Jun 2
Flexible Online Representation Learning Based on Similarity Matching
arXiv:2606. 01546v1 Announce Type: new Abstract: Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data.
arXiv AI
Aug 10
Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
arXiv:2608. 06402v1 Announce Type: new Abstract: Community detection is a fundamental task in graph analytics that aims to identify cohesive groups of entities with similar behaviors or interests.
arXiv Machine Learning
Jun 19
Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities
arXiv:2509. 15822v3 Announce Type: replace-cross Abstract: Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number $K$ of communities is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold.
arXiv Machine Learning
5d ago
Embedding networks with the random walk first return time distribution
arXiv:2512. 02694v3 Announce Type: replace-cross Abstract: We propose the first return time distribution (FRTD) of a random walk as an interpretable and mathematically grounded node embedding.
arXiv Machine Learning
Jun 5
Sharp Low-Degree Thresholds for Planted-vs-Planted Testing
arXiv:2606. 05266v1 Announce Type: new Abstract: We establish the first sharp thresholds for low-degree polynomial tests in planted-vs-planted settings, where the goal is to determine with vanishing error which of two structured planted mechanisms generated the observed data.