关键词: 社区划分, 增量学习, 模块度

Abstract: There are many static algorithms in the community detection fields but very few of them are able to fit into the current network circumstances where network sizes are getting larger and small-scale changes are getting more frequent. To deal with the limitation, this paper proposes an incremental learning algorithm of community detection based on modularity, which takes consideration of two basic kinds of community operations, edge changes and point changes, and the incremental learning process is carried out with guidance of the principle of modularity maximization. Experimental evaluation shows that the incremental learning algorithm is capable of partitioning new timely changed nodes into existed communities rapidly without any devastation to primitive divisions. Meanwhile, the result is proved to get close to that gained by static algorithm thus saving time and keeping real-time.

Key words: community detection, incremental learning, modularity