EdMot：一种用于基元感知社区发现的边缘增强方法
EdMot: An Edge Enhancement Approach for Motif-aware Community Detection
摘要：
网络社区发现是网络分析领域的一个研究热点。虽然已经提出了许多方法用于社区发现，但它们中的大多数仅考虑在单个节点和边的水平上的网络的低阶结构。因此，它们无法在小的密集子图模式的级别上捕获高阶特征，例如，图案最近，一些高阶方法已经被开发出来，但它们通常集中在基于模体的超图，假设是一个连通图。然而，这种假设在一些现实世界的网络中不能得到保证。特别地，超图可能变得碎片化。也就是说，它可能由大量的连通分量和孤立节点组成，尽管原始网络是一个连通图。因此，现有的高阶方法将严重遭受上述碎片化问题，因为在这些方法中，超图中没有连接的节点即使属于同一社区也不能被分组在一起。为了解决上述碎片化问题，我们提出了一种边缘增强方法的基序感知社区发现（EdMot）。主要思想如下。首先构造一个基于模体的超图，并对超图中的前K个最大连通分支进行模块划分。然后，通过构造一个边集，从每个模块中导出一个团，来加强每个模块内的连通性结构。基于新的边集，输入网络的原始连通性结构被增强以生成重新布线的网络，从而利用基于模体的高阶结构，并且超图碎片问题被很好地解决。最后，重新布线的网络进行划分，以获得更高的阶社区结构。
英文摘要：
Network community detection is a hot research topic in network analysis. Although many methods have been proposed for community detection, most of them only take into consideration the lower-order structure of the network at the level of individual nodes and edges. Thus, they fail to capture the higher-order characteristics at the level of small dense subgraph patterns, e.g., motifs. Recently, some higher-order methods have been developed but they typically focus on the motif-based hypergraph which is assumed to be a connected graph. However, such assumption cannot be ensured in some real-world networks. In particular, the hypergraph may become fragmented. That is, it may consist of a large number of connected components and isolated nodes, despite the fact that the original network is a connected graph. Therefore, the existing higher-order methods would suffer seriously from the above fragmentation issue, since in these approaches, nodes without connection in hypergraph can't be grouped together even if they belong to the same community. To address the above fragmentation issue, we propose an Edge enhancement approach for Motif-aware community detection (EdMot). The main idea is as follows. Firstly, a motif-based hypergraph is constructed and the top K largest connected components in the hypergraph are partitioned into modules. Afterwards, the connectivity structure within each module is strengthened by constructing an edge set to derive a clique from each module. Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed. Finally, the rewired network is partitioned to obtain the higher-order community structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
