This study considers the problem of hybrid community detection in attributed networks based on the information of network topology and attributes with the aim to address the following two shortcomings of existing hybrid community detection methods. First, many of these methods are based on the assumption that network topology and attributes carry consistent information but ignore the intrinsic mismatch correlation between them. Second, network topology is typically treated as the dominant source of information, with attributes employed as the auxiliary source; the dominant effect of attributes is seldom explored or indeed considered. To address these limitations, this paper presents a novel Dual-channel Hybrid Community Detection (DHCD) method that considers the dominant effects of topology and attributes separately. The concept of transition relation between the topology and attribute clusters is introduced to explore the mismatch correlation between the two sources and learn the behavioral and content diversity of nodes. An extended overlapping community detection algorithm is introduced based on the two types of diversity. By utilizing network attributes, DHCD can simultaneously derive the community partitioning membership and corresponding semantic descriptions. The superiority of DHCD over state-of-the-art community detection methods is demonstrated on a set of synthetic and real-world networks.

本研究基于网络拓扑和属性信息，考虑属性网络中的混合社区检测问题，旨在解决现有混合社区检测方法的以下两个缺点。首先，这些方法中的许多方法都是基于这样的假设：网络拓扑和属性携带一致的信息，但是忽略了它们之间的固有失配相关性。其次，网络拓扑通常被视为主要的信息源，而属性被用作辅助源。很少探讨或确实不考虑属性的主导作用。为了解决这些限制，本文提出了一种新颖的双通道混合社区检测（DHCD）方法，该方法分别考虑了拓扑和属性的主要影响。引入拓扑和属性簇之间的过渡关系的概念，以探索两个源之间的不匹配关系，并了解节点的行为和内容多样性。引入了基于两种多样性的扩展重叠社区检测算法。通过利用网络属性，DHCD可以同时导出社区划分成员资格和相应的语义描述。DHCD相对于最新的社区检测方法的优越性在一组合成的和真实的网络上得到了证明。引入了基于两种多样性的扩展重叠社区检测算法。通过利用网络属性，DHCD可以同时导出社区划分成员资格和相应的语义描述。DHCD相对于最新的社区检测方法的优越性在一组合成的和真实的网络上得到了证明。引入了基于两种多样性的扩展重叠社区检测算法。通过利用网络属性，DHCD可以同时导出社区划分成员资格和相应的语义描述。DHCD相对于最新的社区检测方法的优越性在一组合成的和真实的网络上得到了证明。