本文選題：社區(qū)發(fā)現(xiàn)　切入點：真實社區(qū)　出處：《電子科技大學》2016年博士論文　論文類型：學位論文

【摘要】：網(wǎng)絡是對許多現(xiàn)實系統(tǒng)的一種既簡單又形象的表達方式,對網(wǎng)絡進行研究有助于人們理解真實世界。許多現(xiàn)實網(wǎng)絡,比如社交網(wǎng)、作者合作網(wǎng)、蛋白質(zhì)交互網(wǎng)、萬維網(wǎng)等,都具有一種重要的中觀結(jié)構——社區(qū)結(jié)構。一個網(wǎng)絡中的社區(qū)結(jié)構是網(wǎng)絡中的社區(qū)劃分,即將節(jié)點劃分到不同的社區(qū),社區(qū)內(nèi)節(jié)點連接更加緊密,而社區(qū)之間的連接相對稀疏。社區(qū)發(fā)現(xiàn)能夠探知網(wǎng)絡的隱藏結(jié)構,發(fā)現(xiàn)網(wǎng)絡的潛在信息,對認識和理解網(wǎng)絡的拓撲結(jié)構起著重要的作用;社區(qū)演變追蹤能夠探知社區(qū)的改變情況,揭示網(wǎng)絡的內(nèi)在動向,對捕捉和掌握網(wǎng)絡的發(fā)展趨勢起著不可忽略的作用。因此,對社區(qū)發(fā)現(xiàn)和社區(qū)演變追蹤開展研究具有重要的意義。在社區(qū)發(fā)現(xiàn)方面,本文研究了個人社交網(wǎng)絡中的社交圈發(fā)現(xiàn)和現(xiàn)實大規(guī)模網(wǎng)絡中的社區(qū)發(fā)現(xiàn),提出了相關的社區(qū)發(fā)現(xiàn)算法。在社區(qū)演變追蹤方面,本文研究了動態(tài)網(wǎng)絡在每個時刻的社區(qū)發(fā)現(xiàn)和相鄰時刻的社區(qū)結(jié)構匹配,提出了相關的社區(qū)演變追蹤算法。本文的主要工作如下:1.針對個人社交網(wǎng)絡中的社交圈發(fā)現(xiàn)問題,提出了基于加強鏈聚類的社交圈發(fā)現(xiàn)算法。社交圈發(fā)現(xiàn)屬于社區(qū)發(fā)現(xiàn),本文在對真實社交圈分析的基礎上,將節(jié)點屬性信息和網(wǎng)絡結(jié)構信息整合到邊上,提出了一種加強鏈聚類算法。實驗結(jié)果表明,與目前的社交圈發(fā)現(xiàn)算法相比,所提出的算法可以更快速更準確地完成個人社交網(wǎng)絡中的社交圈發(fā)現(xiàn)。2.為了能更加準確地發(fā)現(xiàn)現(xiàn)實大規(guī)模網(wǎng)絡中的社區(qū),提出了兩種基于加權策略的社區(qū)發(fā)現(xiàn)算法。首先研究了大規(guī)模網(wǎng)絡中的真實社區(qū)結(jié)構,發(fā)現(xiàn)了社區(qū)結(jié)構具有的一種性質(zhì);然后基于此設計了一種加權策略,并在此加權策略的基礎上提出了兩種社區(qū)發(fā)現(xiàn)算法。在現(xiàn)實網(wǎng)絡上的實驗結(jié)果表明,所提出的基于加權策略的算法可以更準確地發(fā)現(xiàn)真實社區(qū)。3.提出一種基于加權局部視圖的社區(qū)發(fā)現(xiàn)算法。該算法結(jié)合分析到的社區(qū)性質(zhì)探索節(jié)點對社區(qū)結(jié)構的局部視圖,然后整合節(jié)點的局部視圖得到社區(qū)結(jié)構。在現(xiàn)實網(wǎng)絡上的實驗結(jié)果表明,所提出的基于加權局部視圖的算法在發(fā)現(xiàn)大規(guī)模網(wǎng)絡中的社區(qū)時存在效率優(yōu)勢,且能更準確地發(fā)現(xiàn)真實社區(qū)。4.在社區(qū)演變追蹤方面,提出了一種增量式的局部動態(tài)社區(qū)演變追蹤算法。該算法分兩個步驟:1)為了快速發(fā)現(xiàn)動態(tài)網(wǎng)絡在每個時刻的社區(qū)結(jié)構,該算法在每個時刻只關注網(wǎng)絡中發(fā)生變化的節(jié)點,通過探索變化節(jié)點的局部視圖對社區(qū)結(jié)構進行更新;2)為了快速地匹配相鄰時刻的社區(qū)結(jié)構以追蹤社區(qū)的演變行為,該算法基于變化節(jié)點在變化前后的社區(qū)歸屬關系,構建一個部分社區(qū)演變圖,并通過搜索部分社區(qū)演變圖對社區(qū)的演變行為進行追蹤。實驗結(jié)果表明,當網(wǎng)絡變化平滑時,所提出的局部動態(tài)社區(qū)演變追蹤算法能更快速地完成社區(qū)演變追蹤;當網(wǎng)絡變化劇烈時,所提出的算法也具有一定的優(yōu)勢。
[Abstract]:The network is a kind of simple and vivid expression of many real systems, the network research can help people understand the real world. Many real networks, such as social networks, the author collaboration network, protein interaction networks, the world wide web, there is a kind of important intermediate structure, community structure community structure. A network is a network of community division, will be divided into different nodes within the community, community and community connections more closely, the connection between the relatively sparse. Community discovery can detect hidden structure of the network, find the potential information network, plays an important role in understanding the network topology; to ascertain the change of community evolution track community, internal trends reveal network, to capture and plays an irreplaceable role to grasp the development trend of the network. Therefore, the community discovery and community play Change tracking research has important significance. Found in the community, this paper studies the personal social network in the social circle and reality found in large-scale network community discovery, put forward the relevant community discovery algorithm. In the evolution of the community tracking, in this paper, the dynamic network community structure at each time was found and the adjacent community moment, put forward the relevant community evolution tracking algorithm. The main work of this paper are as follows: 1. for the personal social network social circle to find problems, put forward to strengthen the clustering algorithm that chain based social circle social circle. That belongs to the community, based on the analysis of the real social circle, the attribute node information and network information integration to the side, put forward a kind of enhanced chain clustering algorithm. The experimental results show that with the current social circle discovery algorithm, proposed the The algorithm can more quickly and more accurately complete the personal social network social circle found.2. more accurately find the real large-scale network community to, put forward two kinds of community discovery algorithm based on weighted strategy. Firstly, the real large-scale network community structure, found a community structure with nature; then based on this design a weighted strategy, and based on the weighted strategy put forward two kinds of community detection algorithms in real network. The experimental results show that the proposed algorithm based on weighted strategy can more accurately find the real community discovery algorithm.3. proposed a weighted partial view of community based on the algorithm. With the exploration of community property of local view of community structure analysis to the local node, then the node view integration community structure. Experimental results on the network in reality The results show that the proposed algorithm based on weighted local view in the discovery of large scale network community has efficiency advantages, and can more accurately find the real community.4. in the community evolution tracking, we propose a local dynamic community an incremental evolution tracking algorithm. The algorithm consists of two steps: 1) in order to quickly find the community structure of the dynamic network at each time, the algorithm only focus on changes in the network nodes in each moment, the community structure was updated by local view to explore changes of nodes; 2) to community structure quickly match the adjacent time to track the evolution of the community, based on the change of the node before and after the changes belonging to the community relations, construct a part of the community evolution map, and search through community evolution evolution behavior of Community Tracking. The experimental results show that when the network changes When smoothing, the proposed local dynamic community evolution tracking algorithm can track community evolution more quickly. When the network changes dramatically, the proposed algorithm has certain advantages.

【學位授予單位】：電子科技大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP393.09;TP311.13

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1 胡艷梅;基于社區(qū)性質(zhì)的社區(qū)發(fā)現(xiàn)和基于局部視圖的社區(qū)演變追蹤研究[D];電子科技大學;2016年

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本文編號：1589993