【摘要】：由于二分網(wǎng)絡(luò)特殊的二分結(jié)構(gòu),使得基于單模網(wǎng)絡(luò)的現(xiàn)有社區(qū)發(fā)現(xiàn)算法無法適用。提出一種基于Kullback-Leibler距離的二分網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)算法,該算法將異質(zhì)節(jié)點間的連接關(guān)系轉(zhuǎn)換為其在用戶節(jié)點集上的連接概率分布,并建立基于概率分布的KL相似度衡量節(jié)點連接模式的差異性,從而克服二分結(jié)構(gòu)對節(jié)點相似性評估的不利影響,實現(xiàn)對二分網(wǎng)絡(luò)異質(zhì)節(jié)點的社區(qū)發(fā)現(xiàn)。在人工網(wǎng)絡(luò)和真實網(wǎng)絡(luò)上的實驗和分析表明,該算法能夠有效挖掘二分網(wǎng)絡(luò)社區(qū)結(jié)構(gòu),改善二分網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)的準確性和效率。
[Abstract]:Due to the special dichotomy structure of binary networks, the existing community discovery algorithms based on single mode networks can not be applied. A community discovery algorithm for binary networks based on Kullback-Leibler distance is proposed. The algorithm converts the connection relationship between heterogeneous nodes into its connection probability distribution on the user node set. The KL similarity based on probabilistic distribution is established to measure the difference of node connection patterns, so as to overcome the adverse effect of binary structure on node similarity evaluation and realize community discovery of heterogeneous nodes in binary networks. Experiments and analysis on artificial network and real network show that the algorithm can effectively mine the binary network community structure and improve the accuracy and efficiency of binary network community discovery.
【作者單位】： 河南工學院計算機科學與技術(shù)系;河南師范大學網(wǎng)絡(luò)中心;
【基金】：河南省高等學校重點科研資助項目(15A520063,16A520083)
【分類號】：O157.5
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本文編號：2279377