本文選題：社交網(wǎng)絡(luò) + 社區(qū)領(lǐng)袖　； 參考：《上海交通大學(xué)》2014年碩士論文

【摘要】：在社交網(wǎng)絡(luò)中，用戶A關(guān)注了用戶B，他們之間便產(chǎn)生了聯(lián)系，眾多的聯(lián)系就會(huì)形成社區(qū)，社區(qū)領(lǐng)袖的挖掘是社交網(wǎng)絡(luò)的一個(gè)研究課題。挖掘社區(qū)領(lǐng)袖意味著要識(shí)別網(wǎng)絡(luò)中的重要節(jié)點(diǎn)，這就涉及到社區(qū)中心性分析，其常用的標(biāo)準(zhǔn)有特征向量中心性。在社交網(wǎng)絡(luò)中，用戶和用戶的聯(lián)系通過(guò)關(guān)注產(chǎn)生，而在網(wǎng)絡(luò)上，網(wǎng)頁(yè)和網(wǎng)頁(yè)的聯(lián)系通過(guò)鏈接產(chǎn)生，兩者之間有共同性。PageRank算法正是眾所周知的用于網(wǎng)頁(yè)排名的算法，本文為此將其借鑒過(guò)來(lái)并加以改進(jìn)生成UserRank算法，使之適用于社區(qū)領(lǐng)袖的挖掘。新算法將傳統(tǒng)上把影響力平均分配給關(guān)注的人的做法，改進(jìn)為依據(jù)用戶間的親密程度不同將影響力按不同的比例分配給關(guān)注的人，，從而實(shí)現(xiàn)了在用戶的關(guān)注關(guān)系上賦予權(quán)重的目的。在社區(qū)中，一個(gè)用戶就是一個(gè)節(jié)點(diǎn)，節(jié)點(diǎn)之間的影響力會(huì)互相傳遞，節(jié)點(diǎn)X關(guān)注了節(jié)點(diǎn)Y，則節(jié)點(diǎn)X的影響力就會(huì)全部或者部分貢獻(xiàn)給節(jié)點(diǎn)Y。經(jīng)過(guò)算法多次迭代計(jì)算后，社區(qū)中每個(gè)用戶的影響力收斂后趨于穩(wěn)定，影響力排名最大的用戶，就是社區(qū)領(lǐng)袖。實(shí)驗(yàn)結(jié)果表明改進(jìn)后的新算法能更快更有效地挖掘出社交網(wǎng)絡(luò)中的社區(qū)領(lǐng)袖。
[Abstract]:In the social network, user A pays attention to user B, they have the connection between them, many connections will form the community, the mining of community leader is a research topic of social network. Mining community leaders means identifying important nodes in the network, which involves community-centric analysis. The commonly used criteria are eigenvector centrality. In social networks, the connection between the user and the user is generated by attention, and on the network, the connection between the web page and the web page is generated by the link, and there is a commonality between the two. PageRank algorithm is known as the algorithm used to rank web pages. In this paper, we use it for reference and improve the generated UserRank algorithm to make it suitable for the mining of community leaders. The new algorithm, which traditionally allocates influence equally to people of concern, is improved by distributing influence to people of concern in different proportions, depending on the degree of intimacy between users. Thus, the purpose of giving weight to the user's concern relationship is realized. In the community, a user is a node, the influence between nodes will be transferred to each other, node X pays attention to node Y. then the influence of node X will contribute to node Y. in whole or in part. After iterative calculation, the influence of each user in the community converges and tends to be stable, and the most influential user is the community leader. Experimental results show that the improved algorithm can find community leaders in social networks more quickly and effectively.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 何東曉;周栩;王佐;周春光;王U

本文編號(hào)：2072151