传统社区发现算法大多考虑因素单一,联系密切的友人间关注点可能差异较大,而关注点相同的用户却又可能不在一个朋友圈内.为此,提出了一种混合社区发现算法HCDA,它既考虑社区网络中的节点关注点,又考虑了社区网络拓扑结构,以社区用户间的公共邻居比、关注度及发布微博相似度为依据,度量相邻节点间的社区关联紧密度.并以此为基础,依据相邻节点间的社区增益值,迭代地扩展社区,发现朋友圈中真正的兴趣小组.实验表明,相较于其他方法,本算法能够更准确的发现社区.%Most traditional community detection algorithms always consider single factor. Friends who have close relationship may have different concerns and users who have common concerns may not be in a circle of friends. To solve the problems, this thesis presents a hybrid community detection algorithm HCDA, which takes into account the concerns of the community network nodes, but also consider the topological structure of community network. On this basis, it expands iteratively the community by the community gain value between adjacent nodes to find the real interest groups among friend circles. The experimental results illustrate that compared with other methods the proposed algorithm can find the community more accurately.
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