𝜈-LPA: A Fast, GPU-Based Label Propagation Algorithm for Community Detection
𝜈-LPA is a highly optimized, parallel implementation of the Label Propagation Algorithm (LPA) for community detection in large-scale networks, leveraging the computational power of GPUs to achieve significant speedups compared to existing CPU-based implementations.
Memory-Efficient Parallel Community Detection on Large Graphs Using Weighted Sketches: A Trade-off Between Speed and Memory Usage
This paper introduces a memory-efficient approach to parallel community detection in large graphs, utilizing weighted Misra-Gries sketches to reduce the memory footprint of Louvain, Leiden, and Label Propagation Algorithms (LPA) at the cost of moderate runtime increase.
VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Edge covariates improve community detection in the VEC-SBM model.
Improving Community Detection Algorithms with Random Walks
The authors propose improvements to community detection algorithms by incorporating random walks, enhancing efficiency and maintaining complexity. Their approach aims to refine clustering results while validating the effectiveness through experiments.
Efficient Overlapping Community Detection Algorithms Using Modularity and Cosine Functions
The authors present two overlapping network community detection algorithms based on extended modularity and cosine functions, applicable to both undirected and directed graphs.
