复杂网络中富三元组子结构的图论刻画
Exploring triad-rich substructures by graph-theoretic characterizations in complex networks
摘要：
在复杂网络中，如何准确地检测元数据组是一个重要的问题。主要的挑战在于，传统的结构化社区并不总是捕捉到元数据组的内在特征。PPI网络中的元数据组往往由大量相互作用的三元组基序组成，基于这一观察，我们定义了一个直径为2的2-俱乐部子结构来描述一个元数据组，该子结构具有丰富的三元组性质.基于三元组丰富的子结构，我们设计了一个DIVision算法，使用我们提出的边缘小生境中心DIVANC来有效地检测复杂网络中的元数据组。我们还扩展DIVANC检测重叠元数据组提出了一个简单的2跳重叠策略。为了验证三元组丰富的子结构的有效性，我们比较了DIVANC与现有的算法在PPI网络，LFR合成网络和足球网络。实验结果表明，DIVANC算法明显优于其他算法，特别是可以检测稀疏元数据组。
英文摘要：
One of the most important problems in complex networks is how to detect metadata groups accurately. The main challenge lies in the fact that traditional structural communities do not always capture the intrinsic features of metadata groups. Motivated by the observation that metadata groups in PPI networks tend to consist of an abundance of interacting triad motifs, we define a 2-club substructure with diameter 2 which possessing triad-rich property to describe a metadata group. Based on the triad-rich substructure, we design a DIVision Algorithm using our proposed edge Niche Centrality DIVANC to detect metadata groups effectively in complex networks. We also extend DIVANC to detect overlapping metadata groups by proposing a simple 2-hop overlapping strategy. To verify the effectiveness of triad-rich substructures, we compare DIVANC with existing algorithms on PPI networks, LFR synthetic networks and football networks. The experimental results show that DIVANC outperforms most other algorithms significantly and, in particular, can detect sparse metadata groups.
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。
