Community discovery is an important task in social network analysis. However, most existing methods for community discovery rely on the topological structure alone. These methods ignore the rich information available in the content data. In order to solve this issue, in this paper, we present a community discovery method based on heterogeneous information network decomposition and embedding. Unlike traditional methods, our method takes into account topology, node content and edge content, which can supply abundant evidence for community discovery. First, an embedding-based similarity evaluation method is proposed, which decomposes the heterogeneous information network into several subnetworks, and extracts their potential deep representation to evaluate the similarities between nodes. Second, a bottom-up community discovery algorithm is proposed. Via leader nodes selection, initial community generation, and community expansion, communities can be found more efficiently. Third, some incremental maintenance strategies for the changes of networks are proposed. We conduct experimental studies based on three real-world social networks. Experiments demonstrate the effectiveness and the efficiency of our proposed method. Compared with the traditional methods, our method improves normalized mutual information (NMI) and the modularity by an average of 12% and 37% respectively.

中文翻译：

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通过分解和嵌入异构信息网络寻找社区

社区发现是社交网络分析中的一项重要任务。然而，大多数现有的社区发现方法仅依赖于拓扑结构。这些方法忽略了内容数据中可用的丰富信息。为了解决这个问题，本文提出了一种基于异构信息网络分解和嵌入的社区发现方法。与传统方法不同，我们的方法考虑了拓扑、节点内容和边缘内容，可以为社区发现提供丰富的证据。首先，提出了一种基于嵌入的相似性评估方法，将异构信息网络分解为多个子网络，并提取其潜在的深层表示来评估节点之间的相似性。其次，提出了一种自底向上的社区发现算法。通过领导节点选择、初始社区生成和社区扩展，可以更有效地找到社区。第三，提出了一些针对网络变化的增量维护策略。我们基于三个真实世界的社交网络进行实验研究。实验证明了我们提出的方法的有效性和效率。与传统方法相比，我们的方法将归一化互信息（NMI）和模块化分别平均提高了 12% 和 37%。实验证明了我们提出的方法的有效性和效率。与传统方法相比，我们的方法将归一化互信息（NMI）和模块化分别平均提高了 12% 和 37%。实验证明了我们提出的方法的有效性和效率。与传统方法相比，我们的方法将归一化互信息（NMI）和模块化分别平均提高了 12% 和 37%。