Author:
Qi Kai,Zhang Heng,Zhou Yang,Liu Yifan,Li Qingxiang
Abstract
AbstractCommunity partitioning is an effective technique for cyberspace mapping. However, existing community partitioning algorithm only uses the topological structure of the network to divide the community and disregards factors such as real hierarchy, overlap, and directionality of information transmission between communities in cyberspace. Consequently, the traditional community division algorithm is not suitable for dividing cyberspace resources effectively. Based on cyberspace community structure characteristics, this study introduces an algorithm that combines an improved local fitness maximization (LFM) algorithm with the PageRank (PR) algorithm for community partitioning on cyberspace resources, called PR-LFM. First, seed nodes are determined using degree centrality, followed by local community expansion. Nodes belonging to multiple communities undergo further partitioning so that they are retained in the community where they are most important, thus preserving the community’s original structure. The experimental data demonstrate good results in the resource division of cyberspace.
Funder
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Reference52 articles.
1. Ni, C. C., Lin, Y. Y., Luo, F. & Gao, J. Community detection on networks with Ricci flow. Sci. Rep. 9, 9984 (2019).
2. Kirkley, A. & Newman, M. E. J. Representative community divisions of networks. Commun. Phys. 5 (2021).
3. Wang, X. et al. Multiscale model construction of elements of point groups in network space by combining Blondel algorithm and the hybrid algorithm of k-core decomposition. J. Geo-Inf. Sci 23, 2128–2138 (2021).
4. Zhu, Z. Hierarchical Representation of Network Map (Wuhan Univ., 2017).
5. Luo, Z. et al. New advances in complex network community detection algorithms. J. Natl. Univ. Def. Technol. 33, 47–52 (2011).