人工知能学会研究会資料 人工知能基本問題研究会
Online ISSN : 2436-4584
96回 (2014/1)
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解集合の分割に基づく極大 k-Plex 抽出の高速化
大久保 好章原口 誠
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p. 09-

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In this paper, we are concerned with a problem of enumerating maximal k-plexes in a given undirected graph, where the notion of k-plex is a relaxation model of clique. An existing algorithm for this task tries to nd solution k-plexes based on a theoretical property of diameter of k-plex . In order to enjoy this propery fully, we propose to divide the class of maximal k-plexes into several subclasses based on size and properness of k-plexes . We, then, observe the maximal k-plexes in each subclass have smaller diameter, where the smaller diameter of k-plex becomes, the less the number of search branches becomes. As a result, we expect that computational cost for our enumeration task can be reduced. Our experimental results for several benchmark graphs show that the proposed approach can achieve a certain degree of improvement in efficiency.

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