代表性社区集发现

Finding Representative Communities

随着计算机的普及和互联网的快速发展,社交网络的使用也越来越普遍。信息在社交网络上呈爆炸式传播,越来越多的热门事件、网络红人在互联网上出现,同时这些事件或者人物会通过联系聚集为不同的群体或者社区,从而产生巨大的影响力。因此,互联网、生物学、经济学等各类学科中的社区问题逐渐成了研究热点,对社区进行研究发展成为了一个新兴的方向。然而,进行社区发现后所得到的数据量仍是庞大的,因此,社区发现相关工作完成之后,对社区发现的结果进行优化和进一步处理的相关研究也逐渐兴起并且受到重视。为了达到以上目的,使社区及其相关理论能够应用到实际中,实现从理论到应用的转型,提出了代表性社区集发现算法。

With the popularization of computer and rapid development of the Internet, the use of social networks is becoming more and more popular. Information in social networks is spreading explosively, increasingly popular events and web celebrities appear on the Internet. These events or people gather into different groups or communities through different connections, and produce enormous leverage. Therefore, communities of the Internet, biology, economics and other disciplines have become a hot topic of research gradually. Researches on the community have become a burgeoning area. The research on community belongs to complex network research area, it has irreplaceable significance in computer science, biology and other disciplines. However, the data size is still huge after community detection. Therefore, once the community detection is completed, how to optimize and what we can do to process the result further are aspects that are gradually on the rise and have been highly atta- ched. In order to achieve the purpose, and to make the community and related theory applied to practice, this paper proposes the representative communities mining algorithm.