2A1-J08 二部グラフ分割問題の順序最適化としての解法(進化・学習とロボティクス)

概要

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There're a various networks which can represent bipartite graph in the real world. Most of the unipartite graph clustering methods have been researched but few of the bipartite graph. The bipartite graph clustering methods have been implemented by the unipartite folding. But this approach results in lack of link information, and existing approaches have a large computation orders. In this paper, we propose the bipartite clustering by TSP solution. We propose three cost function definitions with vertex adjacency vectors. Then, we apply the solution to computer-generated graphs with Local Clustering Organization(LCO). As results of the experiment, we obtained clusters by cutting high cost threshold, and we found that the cost function based on cosine construct better clusters.
一般社団法人日本機械学会の論文
2007-05-11