面向微博用戶的社交網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)研究
發(fā)布時(shí)間：2018-11-23 10:41
【摘要】：隨著計(jì)算機(jī)技術(shù)快速發(fā)展,社交網(wǎng)絡(luò)應(yīng)運(yùn)而生改變了人們面對(duì)面的交流方式,由傳統(tǒng)的線下溝通變革為新時(shí)代的“線上”溝通及“掌心”交流。如今處于大數(shù)據(jù)時(shí)代,社交網(wǎng)絡(luò)中海量數(shù)據(jù)對(duì)于社會(huì)科學(xué)研究顯得更加重要,而發(fā)現(xiàn)社交網(wǎng)絡(luò)的社區(qū)結(jié)構(gòu)已然成為學(xué)者們研究的熱點(diǎn)領(lǐng)域。社區(qū)發(fā)現(xiàn)技術(shù)對(duì)研究復(fù)雜網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)有很大的幫助,同時(shí)也蘊(yùn)含很大的社會(huì)價(jià)值。目前在社區(qū)發(fā)現(xiàn)技術(shù)在復(fù)雜網(wǎng)絡(luò)領(lǐng)域已經(jīng)取得了不錯(cuò)的成果,但針對(duì)社交網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)技術(shù)還不太成熟,因?yàn)樯缃痪W(wǎng)絡(luò)規(guī)模較大且內(nèi)容繁雜,大多算法都存在一定的缺陷,如算法復(fù)雜度過高,結(jié)果不夠準(zhǔn)確或局部最優(yōu)等。鑒于此,本文針對(duì)社交網(wǎng)絡(luò)中的微博網(wǎng)絡(luò)進(jìn)行研究,從微博用戶出發(fā),通過用戶關(guān)系和用戶內(nèi)容的融合,發(fā)現(xiàn)潛在的用戶社區(qū),并且通過實(shí)驗(yàn)證實(shí)了結(jié)果的合理性。本文主要做了以下幾方面的研究和創(chuàng)新:(1)針對(duì)微博網(wǎng)絡(luò)中用戶關(guān)系結(jié)構(gòu)的特點(diǎn),考慮到網(wǎng)絡(luò)中同時(shí)存在單向關(guān)注和雙向關(guān)注兩類關(guān)系,提出了一種計(jì)算用戶關(guān)系相似度的方法,該方法兼顧這兩類關(guān)注關(guān)系對(duì)節(jié)點(diǎn)的影響,同時(shí)將有向網(wǎng)絡(luò)轉(zhuǎn)換為加權(quán)無向網(wǎng)絡(luò)進(jìn)行計(jì)算,提高了運(yùn)行效率。另外針對(duì)加權(quán)無向網(wǎng)絡(luò),利用用戶相似度作為權(quán)重提出了一種改進(jìn)的CNM社區(qū)發(fā)現(xiàn)算法。根據(jù)朋友的朋友更容易成為朋友的思想,可以延伸為朋友的朋友和自己同相似,所以用節(jié)點(diǎn)相似度替代模塊度進(jìn)行社區(qū)合并,更加合理的發(fā)現(xiàn)用戶社區(qū)。這是針對(duì)網(wǎng)絡(luò)中用戶的關(guān)系特點(diǎn)進(jìn)行社區(qū)發(fā)現(xiàn)。(2)微博網(wǎng)絡(luò)中用戶內(nèi)容可以反映用戶當(dāng)前的興趣,針對(duì)這一思想,提出了用戶關(guān)系和用戶內(nèi)容融合的社區(qū)發(fā)現(xiàn)算法。根據(jù)主題模型的思想融入用戶標(biāo)簽來發(fā)現(xiàn)用戶的興趣主題,通過相對(duì)熵計(jì)算用戶興趣主題的相似度,同時(shí)加入用戶關(guān)系相似度并通過實(shí)驗(yàn)調(diào)節(jié)兩類相似度融合的比重,充分體現(xiàn)用戶的興趣特性。(3)在融合用戶關(guān)系和內(nèi)容兩種相似度的基礎(chǔ)上,提出了JSCNM算法,利用改進(jìn)模塊度增量函數(shù)將融合后的中心度加入到優(yōu)化函數(shù)中,充分考慮微博網(wǎng)絡(luò)中關(guān)系和內(nèi)容對(duì)節(jié)點(diǎn)影響力作用,經(jīng)過不斷尋找最優(yōu)目標(biāo)達(dá)到劃分社區(qū)目的。利用微博網(wǎng)絡(luò)真實(shí)數(shù)據(jù)集進(jìn)行實(shí)驗(yàn),結(jié)果證明劃分社區(qū)更加合理。
[Abstract]:With the rapid development of computer technology, social networks have changed the way people communicate face to face, from the traditional offline communication to the new era of "online" communication and "palm" communication. In the era of big data, the mass data in social network is more important for social science research, and the discovery of social network community structure has become a hot research area of scholars. Community discovery technology is of great help to the study of complex network topology, and also has great social value. At present, the community discovery technology has made good achievements in the field of complex network, but the community discovery technology for social network is not very mature, because the social network is large and complex, most algorithms have some defects. If the complexity of the algorithm is too high, the results are not accurate or local optimal. In view of this, this paper focuses on Weibo network in social network. From the perspective of Weibo users, through the fusion of user relationship and user content, the potential user community is discovered, and the rationality of the result is verified by experiments. This paper mainly makes the following aspects of research and innovation: (1) considering the characteristics of user relationship structure in Weibo network, considering that there are two kinds of relationships in the network: unidirectional concern and two-way concern, This paper presents a method to calculate the similarity of user relationships. This method takes into account the influence of these two kinds of relationships on nodes, and transforms the directed network into a weighted undirected network for computation, which improves the running efficiency. In addition, an improved CNM community discovery algorithm based on user similarity is proposed for weighted undirected networks. According to the idea that a friend of a friend is more likely to be a friend, it can be extended to a friend who is similar to himself, so the node similarity degree is used instead of the module degree to merge the community and to find the user community more reasonably. This is based on the characteristics of user relationships in the network. (2) user content in Weibo network can reflect the current interests of users. In view of this idea, a community discovery algorithm of user relationship and user content fusion is proposed. According to the idea of topic model, the user's topic of interest is found by integrating the idea of topic model into user's label, and the similarity of user's topic of interest is calculated by relative entropy. At the same time, the similarity of user relationship is added and the proportion of the fusion of two kinds of similarity is adjusted by experiment. (3) on the basis of merging user relationship and content similarity, JSCNM algorithm is proposed, and the improved modularity increment function is used to add the merged centrality to the optimization function. Considering the influence of the relationship and content on the nodes in Weibo's network, the goal of dividing the community is achieved by searching for the optimal goal. Using Weibo network real data set to experiment, the results show that the division of community is more reasonable.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09
[Abstract]:With the rapid development of computer technology, social networks have changed the way people communicate face to face, from the traditional offline communication to the new era of "online" communication and "palm" communication. In the era of big data, the mass data in social network is more important for social science research, and the discovery of social network community structure has become a hot research area of scholars. Community discovery technology is of great help to the study of complex network topology, and also has great social value. At present, the community discovery technology has made good achievements in the field of complex network, but the community discovery technology for social network is not very mature, because the social network is large and complex, most algorithms have some defects. If the complexity of the algorithm is too high, the results are not accurate or local optimal. In view of this, this paper focuses on Weibo network in social network. From the perspective of Weibo users, through the fusion of user relationship and user content, the potential user community is discovered, and the rationality of the result is verified by experiments. This paper mainly makes the following aspects of research and innovation: (1) considering the characteristics of user relationship structure in Weibo network, considering that there are two kinds of relationships in the network: unidirectional concern and two-way concern, This paper presents a method to calculate the similarity of user relationships. This method takes into account the influence of these two kinds of relationships on nodes, and transforms the directed network into a weighted undirected network for computation, which improves the running efficiency. In addition, an improved CNM community discovery algorithm based on user similarity is proposed for weighted undirected networks. According to the idea that a friend of a friend is more likely to be a friend, it can be extended to a friend who is similar to himself, so the node similarity degree is used instead of the module degree to merge the community and to find the user community more reasonably. This is based on the characteristics of user relationships in the network. (2) user content in Weibo network can reflect the current interests of users. In view of this idea, a community discovery algorithm of user relationship and user content fusion is proposed. According to the idea of topic model, the user's topic of interest is found by integrating the idea of topic model into user's label, and the similarity of user's topic of interest is calculated by relative entropy. At the same time, the similarity of user relationship is added and the proportion of the fusion of two kinds of similarity is adjusted by experiment. (3) on the basis of merging user relationship and content similarity, JSCNM algorithm is proposed, and the improved modularity increment function is used to add the merged centrality to the optimization function. Considering the influence of the relationship and content on the nodes in Weibo's network, the goal of dividing the community is achieved by searching for the optimal goal. Using Weibo network real data set to experiment, the results show that the division of community is more reasonable.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09
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