基于社团划分的网络聚类布局算法

复杂网络日益受到广大专家和学者们的关注，对其进行可视化展示可以帮助用户发现复杂网络表征的复杂系统中隐藏的知识信息，对计算机科学、社会学、生物学等领域具有重要的意义。力导引布局算法是复杂网络可视化领域的主流算法，它用节点连接图的形式对复杂网络进行抽象表示，布局遵循一定的美学标准如节点的均匀分布、边长尽量一致等，这在一定程度上阻碍了对复杂网络的社团结构的展示。针对以上问题，本文提出引入基于度中心性的社团斥力与引力对力导引算法进行改进，以对复杂网络进行聚类布局。实验结果表明，本文算法可有效地展示复杂网络的社团结构，同时又能保留社团之间边缘节点的信息。

Network Clustering Layout Algorithm Based on Detecting Community Structure

The complex network is becoming increasingly concerned by the experts and scholars. The visualization of the complex network can help the users to discover the hidden knowledge and information in the complex system represented by complex network, which is of great significance to the fields of computer science, sociology, and biology. The force-directed layout algorithm is the mainstream algorithm in the field of complex network visualization. It uses the form of node connection graph to abstract the complex network, the layout follows aesthetic standards such as the uniform distribution of nodes and the uniform of edges, to a certain extent, which hinders the display of the community structure of complex networks. Aiming at above problems, this paper introduces the repulsion and gravitational force of the community based on the degree centrality to improve the clustering layout of the complex network. The experimental results show that the proposed algorithm can effectively display the community structure of complex networks while preserving the information of margin nodes between communities.