本文選題：社交網(wǎng)絡 + 重疊社區(qū)發(fā)現(xiàn)�。� 參考：《合肥工業(yè)大學》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術的快速發(fā)展,社交網(wǎng)絡已逐漸成為了人們?nèi)粘＝挥褱贤�、個人生活展示及消息發(fā)布的主要平臺。社區(qū)發(fā)現(xiàn)是社交網(wǎng)絡研究中的一個熱點,挖掘社交網(wǎng)絡中潛在的社區(qū)結構有助于深入理解網(wǎng)絡的拓撲結構特點,也能為輿情監(jiān)測、意見領袖發(fā)現(xiàn)和個性化推薦等諸多方面的研究與應用提供有力的支持。但目前隨著社交網(wǎng)絡規(guī)模的不斷增大,如何從愈發(fā)復雜的社交網(wǎng)絡中簡單高效地挖掘出具有潛在特征的重疊社區(qū)結構成為了一項具有挑戰(zhàn)性的問題。同時,社交網(wǎng)絡中的用戶之間不僅存在著顯性關系,還存在好友相似、屬性相似和興趣相似等形式所表現(xiàn)出的隱性關系。為了更加合理地分析社交網(wǎng)絡中用戶之間的關系特征,可以通過使用信任來衡量用戶間的個體權重、關系強度等關系屬性,并通過定義信任的計算方法與傳遞規(guī)則來完成社交網(wǎng)絡中的關系描述,從而能夠有效提升社交網(wǎng)絡分析的準確性因此,為解決已有社區(qū)發(fā)現(xiàn)算法中存在的問題,本文首先定義了一種社交網(wǎng)絡中節(jié)點間信任的計算方法,通過使用信任來描述節(jié)點之間的關系特征,并在此基礎之上提出了基于節(jié)點間信任的重疊社區(qū)發(fā)現(xiàn)算法,最后通過對比實驗完成了算法的驗證。具體的研究內(nèi)容如下:1)提出了一種社交網(wǎng)絡中融合了節(jié)點間關系強度與相似性的信任計算方法。在相關信任計算研究的基礎之上,分析與選擇社交網(wǎng)絡中影響節(jié)點間信任的因素之后,針對由節(jié)點間關系強度產(chǎn)生的關系信任和節(jié)點間相似性產(chǎn)生的相似信任,分別給出了對應的計算方法。社交網(wǎng)絡環(huán)境中信任的計算方法是本文后續(xù)研究的重要基礎。2)設計了一種社交網(wǎng)絡中基于節(jié)點間信任的局部重疊社區(qū)發(fā)現(xiàn)算法TLCDA(Trust-Based Local Overlapping Community Detection Algorithm)。TLCDA算法將社交網(wǎng)絡抽象成為數(shù)據(jù)場后使用信任勢來描述局部范圍內(nèi)節(jié)點之間的影響作用,并通過使用粗糙K-Mediods聚類完成重疊社區(qū)發(fā)現(xiàn)。3)制定了實驗方案并完成對比分析。本文選取了LFR人工基準網(wǎng)絡、經(jīng)典真實網(wǎng)絡和微博網(wǎng)絡三種不同類型的網(wǎng)絡,給出了社區(qū)發(fā)現(xiàn)的效果評價指標,并通過與經(jīng)典的社區(qū)發(fā)現(xiàn)算法進行對比完成了TLCDA算法的效果驗證。
[Abstract]:With the rapid development of Internet technology, social network has gradually become the main platform for people to make friends and communicate, personal life display and news release. Community discovery is a hot topic in the research of social network. Mining the potential community structure in social network can help to understand the topological characteristics of the network and monitor the public opinion. Research and application of opinion leader discovery and personalized recommendation provide strong support. However, with the increasing scale of social network, how to find the overlapping community structure with potential characteristics from the increasingly complex social network has become a challenging problem. At the same time, there are not only dominant relationships among users in social networks, but also hidden relationships in the form of similar friends, similar attributes and similar interests. In order to analyze the relationship characteristics of users in social network more reasonably, we can use trust to measure the individual weight and relationship strength among users. The relationship description in social network can be completed by defining trust calculation method and transfer rule, which can effectively improve the accuracy of social network analysis. Therefore, in order to solve the problems existing in existing community discovery algorithms, This paper first defines a computing method of trust between nodes in social networks, describes the relationship characteristics between nodes by using trust, and then proposes an overlapping community discovery algorithm based on trust between nodes. Finally, the algorithm is verified by contrast experiment. The main contents of this paper are as follows: (1) A trust computing method which combines the strength and similarity of the relationship between nodes in social networks is proposed. Based on the research of related trust computing, this paper analyzes and selects the factors that affect the trust between nodes in social network, and then analyzes the relationship trust generated by the strength of the relationship between nodes and the similarity between nodes. The corresponding calculation methods are given respectively. Trust computing method in social network environment is an important foundation of the following research in this paper. (2) A local overlapping community discovery algorithm based on trust between nodes in social network is designed. TLCDA Trust-Based Local overlapping Community Detection algorithm. TLCDA algorithm abstracts social network. After becoming a data field, a trust potential is used to describe the effects between nodes in a local scope. By using rough K-Mediods clustering to complete the overlapping community discovery. 3) the experimental scheme was developed and the comparative analysis was completed. In this paper, we select three different types of networks: LFR-artificial benchmark network, classical real network and Weibo network, and give the evaluation index of community discovery effect. The effect of TLCDA algorithm is verified by comparing with the classical community discovery algorithm.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP301.6;TP393.09

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