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中文摘要：
社区结构是一个重要网络特征。能识别社区能在利用并且理解两个社会 andnon 社会的网络提供无价的帮助。几个算法直到现在被开发了。然而，所有这些算法能与顺序 10 ~ 的顶点仅仅与小或中等的网络工作很好 4。而且，所有存在算法是离线的并且不能与象网，在网，页经常被更新那样的高度有活力的网络工作很好。当一个已经聚类的网络被更新时，包括原来、增长的部分的全部网络不得不是 recalculated，尽管 onlys 光变化被包含。处理这个问题，一个增量算法被建议，它允许在大规模、动态的网络的采矿社区结构。基于以前检测的社区结构，算法几乎不花小时间到重新分类包括两个的全部网络原来、增长的部分。而且，算法比大多数象 Girvan 和纽曼那样的存在算法快“ s 算法和它的改进版本。另外，算法能帮助在网络设想这些社区结构并且提供一条新途径在动态网络的演变进程上研究。
英文摘要：
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
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