Compression layout for network visualization based on node importance for community structure

被引：0
|
作者：
Wu L. [1 ]
Zhang X. [1 ,2 ]
Meng X. [1 ]
机构：
[1] School of Space Information, Space Engineering University, Beijing
[2] Department of Space Command, Space Engineering University, Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 12期
关键词：
Compression layout; Force-directed layout; Network visualization; Node importance; Topological potential;
D O I：
10.13700/j.bh.1001-5965.2019.0385
中图分类号：
学科分类号：
摘要：
In order to effectively display the mesoscale structure of the network, the force-directed layout algorithm is combined with the network community structure features, and a network visualization compression layout method based on the importance of the node in the community structure is proposed. First, the Louvain algorithm is used to divide the network into multi-granular community structure. Then, the importance of the nodes is evaluated by calculating the topological potential of the nodes in the community structure. Through preserving the important nodes, the community is compressed, while the boundary nodes are merged. Finally, the force-directed layout algorithm is adopted to layout the network to achieve a visual compression layout. The experimental results show that the proposed method can completely preserve the original network community structure on the basis of compression nodes and connected edges, and can clearly display the internal structure of the community by retaining the representative points of the community structure, highlighting the position and role of the community and important nodes in the network structure. © 2019, Editorial Board of JBUAA. All right reserved.
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页码：2423 / 2430
页数：7
相关论文
共 17 条
  • [1] Zhang X.T., Wu L.D., Yu S.B., A multi-force directed layout algorithm for multilayer networks visualization, Journal of Computer-Aided Design & Computer Graphics, 31, 4, pp. 639-646, (2019)
  • [2] Yao Z.H., Wu L.D., Song H.C., Problems of network simplification by edge bundling, Journal of Beijing University of Aeronautics and Astronautics, 41, 5, pp. 871-878, (2015)
  • [3] Leskovec J., Faloutsos C., Sampling from large graphs, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 631-636, (2006)
  • [4] Du X.L., Research on visualization problems and algorithms for large scale social network, pp. 18-35, (2015)
  • [5] Gilbert A.C., Levchenko K., Compressing network graphs, Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1-10, (2004)
  • [6] Almendral J.A., Criado R., Leyva I., Et al., Introduction to focus issue: Mesoscales in complex networks, Chaos, 21, 1, (2011)
  • [7] Eades P., A heuristic for graph drawing, Congressus Numerantium, 42, pp. 149-160, (1984)
  • [8] Kamada T., Kawai S., An algorithm for drawing general undirected graphs, Information Processing Letters, 31, 1, pp. 7-15, (1989)
  • [9] Davidson R., Harel D., Drawing graphs nicely using simulated annealing, ACM Transactions on Graphics, 15, 4, pp. 301-331, (1996)
  • [10] Fruchterman T.M.J., Reingold E.M., Graph drawing by force-directed placement, Software-Practice and Experience, 21, 11, pp. 1129-1164, (1991)
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