2014/08/31 by Tao Wu, Wu, Tao, Yuxiao Guo +6
Computer Science · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Mental Health Research Topics #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1409.0205
22 pages, 11 figures, 7 tables. arXiv admin note: text overlap with arXiv:physics/0607100 by other authors
openalex publication_date 2014/08/31 · arxiv created 2015/12/29 · arxiv updated 2015/12/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The investigation of network structure has important significance to understand the functions of various complex networks. The communities with hierarchical and overlapping structures and the special nodes like hubs and outliers are all common structure features to the networks. Network structure investigation has attracted considerable research effort recently. However, existing studies have only partially explored the structure features. In this paper, a label propagation based integrated network structure investigation algorithm (LINSIA) is proposed. The main novelty here is that LINSIA can uncover hierarchical and overlapping communities, as well as hubs and outliers. Moreover, LINSIA can provide insight into the label propagation mechanism and propose a parameter-free solution that requires no prior knowledge. In addition, LINSIA can give out a soft-partitioning result and depict the degree of overlapping nodes belonging to each relevant community. The proposed algorithm is validated on various synthetic and real-world networks. Experimental results demonstrate that the algorithm outperforms several state-of-the-art methods.