Community detection problem is a projection of data clustering where the network's topological properties are only considered for measuring similarities among nodes. Also, finding communities' kernel nodes and expanding a community from kernel will certainly help us to find optimal communities. Among the existing community detection approaches, the affinity propagation (AP)-based method has been showing promising results and does not require any predefined information such as the number of clusters (communities). AP is an exemplar-based clustering method that defines the negative real-valued similarity measure sim(i, k) between data point i and exemplar k as the probability of k being the exemplar of data point i. According to our intuition, the value of sim(i, k) should not be identical to sim(k, i). In this study, a new version of AP using an adaptive similarity matrix, namely affinity propagation with adaptive similarity (APAS) matrix, is proposed, which could efficiently show the leadership probabilities between data points. APAS can adaptively transform the symmetric similarity matrix into an asymmetric one. It outperforms AP method in terms of accuracy. Extensive experiments conducted on artificial and real-world networks demonstrate the effectiveness of our approach.

中文翻译：

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使用具有自适应相似度矩阵的亲和力传播在社交网络中进行社区检测。

社区检测问题是对数据群集的一种预测，其中仅考虑网络的拓扑属性来测量节点之间的相似性。此外，找到社区的内核节点并从内核扩展社区肯定会帮助我们找到最佳社区。在现有的社区检测方法中，基于亲和力传播（AP）的方法已显示出令人鼓舞的结果，并且不需要任何预定义的信息，例如群集（社区）的数量。AP是限定负实数值的相似性度量基于示例性的聚类方法SIM（I，K）之间的数据点我和范例ķ作为概率ķ是数据的示范性点我。根据我们的直觉，sim（i，k）的值不应与sim（k，i）相同。在这项研究中，提出了一种新版本的使用自适应相似性矩阵的AP，即具有自适应相似性的亲和力传播（APAS）矩阵，它可以有效地显示数据点之间的领导概率。APAS可以将对称相似矩阵自适应地转换为非对称矩阵。就准确性而言，它优于AP方法。在人工和现实网络上进行的大量实验证明了我们方法的有效性。