In this paper, a fuzzy agglomerative (FuzAg) approach is proposed for community detection that iteratively updates membership degree of nodes. Earlier approaches assign membership degree to nodes based on communities only. We introduce the notion of self-membership in addition to the membership of different communities. The essence of self-membership is to give opportunity to all nodes in growing their own community. Nodes having higher self-membership degree are referred as anchors, and they get a chance to expand their associated community. Meanwhile, some new anchors may emerge in successive iterations, whereas false or redundant anchors get removed. The time complexity of the proposed algorithm is shown to be $O(n^2)$. We compare the results of the proposed FuzAg algorithm with those of state-of-the-art fuzzy community detection algorithms on ten real-world datasets as well as on synthetic networks. Results indicated by various quality and accuracy metrics show impressive performance of FuzAg in identifying both disjoint communities and fuzzy communities.

中文翻译：

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FuzAg：模糊凝聚社区检测

在本文中，提出了一种用于社区检测的模糊凝聚（FuzAg）方法，该方法迭代更新节点的隶属度。早期的方法仅基于社区为节点分配成员资格。我们引入了概念自我会员除了不同社区的成员。自我会员资格的本质是为所有节点提供发展自己社区的机会。自隶属度较高的节点称为锚，他们有机会扩大他们的相关社区。同时，在连续迭代中可能会出现一些新的锚点，而虚假或多余的锚点 被移除。所提出算法的时间复杂度为 $O(n^2)$. 我们将所提出的 FuzAg 算法的结果与最先进的模糊社区检测算法在十个真实世界数据集以及合成网络上的结果进行了比较。各种质量和准确度指标表明的结果表明 FuzAg 在识别不相交社区和模糊社区方面的表现令人印象深刻。