大规模动态网络社区检测的并行层次方法
计算机视觉与模式识别
2025-02-27 v1
摘要
In this paper, we propose a novel parallel hierarchical Leiden-based algorithm for dynamic community detection. The algorithm, for a given batch update of edge insertions and deletions, partitions the network into communities using only a local neighborhood of the affected nodes. It also uses the inner hierarchical graph-based structure, which is updated incrementally in the process of optimizing the modularity of the partitioning. The algorithm has been extensively tested on various networks. The results demonstrate promising improvements in performance and scalability while maintaining the modularity of the partitioning.
引用
@article{arxiv.2502.18496,
title = {Physical Depth-aware Early Accident Anticipation: A Multi-dimensional Visual Feature Fusion Framework},
author = {Hongpu Huang and Wei Zhou and Chen Wang},
journal= {arXiv preprint arXiv:2502.18496},
year = {2025}
}