融合结构与属性视图的可重叠社区发现算法

社区发现算法是发现社区内部结构和组织原则的基本工具。现有的基于模型的算法和基于优化的算法通常考虑2种信息源，即网络结构和节点属性，以获得具有更密集的网络结构和相似属性信息的社区。然而此类算法在聚类过程中无法自动确定结构与属性之间的相对重要性，以揭示子空间，因此检测到的社区质量还需提升。将子空间集成到一个重叠社区发现框架中，设计了自适应结构和属性权重策略，有效地揭示子空间，从而发现多样性的社区。在人工和真实网络上进行了广泛的实验，进一步分析验证了揭示子空间对于捕获更好的社区的重要性，说明了本文算法的合理性和有效性。

An overlapping community detection method combining structure and attribute view

Community detection algorithms are the basic tools for discovering the internal structure and organizational principles of a community. Existing model-based and optimization-based algorithms usually consider two sources of information: network structure and node attributes, to obtain communities with both denser network structure and similar attribute information. However, such algorithms cannot automatically determine the relative importance between them and reveal subspaces, so the quality of the detected communities needs to be improved. This paper integrates subspaces into a new overlapping community detection framework, and designs an adaptive structure and attribute weighting strategy, which effectively reveals the subspace to discover diverse communities. Extensive experiments are conducted on artificial and real networks. The experimental results reveal the importance of subspaces on capturing better communities, justifying the rationality and effectiveness of the proposed algorithm.