中文摘要：

由于传统的力导引布局方法大都无法展示复杂网络的社团结构,提出一种可有效展示复杂网络社团结构的布局算法——社团引力导引的布局算法.该算法在力导引布局算法的基础上对每个节点加入社团引力,并引入k-means算法,使同一社团的节点能够向社团的中心位置聚拢.不同于先网络聚类再可视化布局的传统做法,该算法不需要预先对节点分类,可以在布局的同时完成节点聚类.实验中使用模块度指标评估社团结构的强弱程度,结果表明,文中算法可以呈现明显的聚类效果,简单、易于实现,且收敛速度快.

英文摘要：

Since most of traditional force-directed algorithms cannot present the community structure of complex networks, this paper proposes a new graph layout method, called community-gravity-directed algorithm, to effectively visualize the community structure of complex networks. Based on the force-directed algorithm, the proposed algorithm introduces community-gravity force to each node, and employs k-means algorithm to make nodes in the same community near to the center of the community. Different from traditional approaches which have to cluster network before visualization, the proposed algorithm can perform the two steps at the same time, without any node pre-classification. In the experiment, modularity is used to evaluate the strength of community structures. Experimental results show that the proposed algorithm can present a significant clustering effect. It is simple, easy to implement, and has a better convergence rate.