Bi-Community Detection Method Based On Both Intra- And Inter-Correlation: An Applied Research Of International Relations

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.