本文選題：社會網(wǎng)絡(luò)分析 + 時域網(wǎng)絡(luò)��； 參考：《應(yīng)用科學學報》2017年05期

【摘要】：針對復(fù)雜網(wǎng)絡(luò)社區(qū)跟蹤中存在忽略演化時域因素以及忽略網(wǎng)絡(luò)成員演化差異性不足等問題,提出一種社區(qū)跟蹤方法.對相似函數(shù)添加時域信息,并考慮網(wǎng)絡(luò)演化的平滑性與節(jié)點間的差異性,提取網(wǎng)絡(luò)中的活躍節(jié)點進行社區(qū)跟蹤.實驗表明,該算法在DBLP數(shù)據(jù)集上能比其他社區(qū)跟蹤算法更好地發(fā)現(xiàn)社區(qū)演化過程,且找到的社區(qū)信息相似度較高.
[Abstract]:Aiming at the problems of neglecting evolution time domain factors and neglecting the difference of evolution of network members in complex network community tracking, a community tracking method is proposed. The time-domain information is added to the similarity function, and considering the smoothness of network evolution and the difference between nodes, the active nodes in the network are extracted for community tracking. Experiments show that the proposed algorithm can detect the evolution process of the community better than other community tracking algorithms on DBLP datasets, and the community information similarity is higher.
【作者單位】： 蘭州交通大學電子信息學院;
【基金】：國家自然科學基金(No.61163010) 蘭州市科技計劃項目基金(No.2014-1-171) 金川公司預(yù)研基金(No.JCYY2013012)資助
【分類號】：O157.5
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本文編號：2024202