The relations between agents of complex networks are generally determined by their attributes, so we can instead study the corresponding bipartite network formed by agents and their attributes to gain a higher-dimensional perspective. General bipartite community detecting algorithms implicitly contain a fixed generation step to determine the intra-correlations of the two separate vertex sets (denoted as instance set and attribute set), thus ignoring problem-related heuristics. Inspired by this, we propose a bi-community detection framework concerning the problem-related features that directly takes such intra-correlations into account, and can be freely combined with different objective functions and optimization algorithms to cope with various network structures such as directed graphs with negative edge weights. The framework is adopted to analyze international relations on the dispute and alliance datasets, whose results contain the relevant events that support the establishment of each community and are highly consistent with Huntington’s theory. In addition, we analyze the impact of the instance–instance, instance–attribute, and attribute–attribute relations on the detection result through control experiments, and conclude that for the general community searching algorithms (including the bi-community case), appropriately taking these three relations together into account can help obtain different reasonable detection results.

复杂网络中Agent之间的关系一般是由Agent的属性决定的，因此我们可以转而研究由Agent及其属性形成的相应的二分网络，以获得更高维度的视角。一般的二分社区检测算法隐式包含固定的生成步骤来确定两个单独的顶点集（表示为实例集和属性集）的内部相关性，从而忽略与问题相关的启发式方法。受此启发，我们提出了一种针对问题相关特征的双社区检测框架，直接考虑这种内部相关性，并且可以自由地与不同的目标函数和优化算法组合，以应对有向图等各种网络结构具有负边权重。采用该框架对争端和联盟数据集进行国际关系分析，其结果包含了支持各个共同体建立的相关事件，与亨廷顿理论高度一致。此外，我们通过控制实验分析了实例-实例、实例-属性、属性-属性关系对检测结果的影响，得出结论：对于一般的社区搜索算法（包括双社区情况），适当地采取将这三种关系综合考虑有助于获得不同的合理的检测结果。