發(fā)布時(shí)間：2019-03-07 21:56

【摘要】：分析了目前動(dòng)態(tài)社區(qū)發(fā)現(xiàn)及其演化所存在的問題,提出了一種新的動(dòng)態(tài)社區(qū)演化方法.該方法利用靜態(tài)社區(qū)挖掘算法提取不同時(shí)間快照的每個(gè)社區(qū),然后計(jì)算出相鄰快照的社區(qū)之間的演化影響力,進(jìn)一步分析連續(xù)快照中社區(qū)結(jié)構(gòu)的發(fā)展演化過程.在新浪微博、網(wǎng)絡(luò)測(cè)量Gnutella等大規(guī)模實(shí)驗(yàn)數(shù)據(jù)集上的驗(yàn)證,證明了該方法的有效性.此外,實(shí)驗(yàn)中還分析了社會(huì)網(wǎng)絡(luò)中節(jié)點(diǎn)的出現(xiàn)和消失的頻繁程度會(huì)影響社區(qū)穩(wěn)定性以及社區(qū)結(jié)構(gòu)的演化.
[Abstract]:This paper analyzes the problems existing in the discovery and evolution of dynamic community, and puts forward a new method of dynamic community evolution. In this method, the static community mining algorithm is used to extract each community with different time snapshots, and then the evolution influence between the communities of adjacent snapshots is calculated, and the evolution process of community structure in continuous snapshots is further analyzed. The validity of this method is proved by experiments on Sina Weibo, Gnutella and other large-scale experimental data sets. In addition, the frequency of the emergence and disappearance of nodes in the social network will affect the stability of the community and the evolution of the community structure.
【作者單位】： 中國(guó)人民大學(xué)信息學(xué)院;淮陰師范學(xué)院計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(71271211);國(guó)家自然科學(xué)基金重點(diǎn)資助項(xiàng)目(71271211)
【分類號(hào)】：TP393.09

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