Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. This paper proposes the embedding-based Silhouette community detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain. Further, we demonstrate that SCD’s outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines.

中文翻译：

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基于嵌入的Silhouette社区检测


以网络形式挖掘复杂数据越来越受到许多科学学科的关注。网络社区对应于密集连接的子网络，通常代表现实世界系统的关键功能部分。本文提出了基于嵌入的 Silhouette 社区检测（SCD），这是一种基于网络节点嵌入（即从其邻域导出的节点的实值表示）的聚类来检测社区的方法。我们研究了所提出的 SCD 方法在 234 个合成网络以及现实生活中的社交网络上的性能。尽管 SCD 不基于任何形式的模块化优化，但它的性能与最先进的社区检测算法（例如 InfoMap 和 Louvain）相当或更好。此外，我们证明 SCD 的输出可以与语义子组发现中的领域本体一起使用，从而对在现实生活中的蛋白质相互作用网络中检测到的社区产生人类可理解的解释。 SCD 基于嵌入，适用范围广泛，并且可以作为许多现有网络学习和探索管道的一部分进行开箱即用的测试。