本文選題：復(fù)雜網(wǎng)絡(luò) + 社區(qū)發(fā)現(xiàn)��； 參考：《華南理工大學(xué)》2015年碩士論文

【摘要】：在復(fù)雜網(wǎng)絡(luò)中,將節(jié)點(diǎn)分成組,組內(nèi)各節(jié)點(diǎn)聯(lián)系十分緊密,組間各節(jié)點(diǎn)聯(lián)系比較稀疏,這種特性稱(chēng)為復(fù)雜網(wǎng)絡(luò)的社區(qū)結(jié)構(gòu)。在大數(shù)據(jù)時(shí)代,準(zhǔn)確發(fā)現(xiàn)社區(qū)結(jié)構(gòu),特別是在大規(guī)模網(wǎng)絡(luò)中準(zhǔn)確發(fā)現(xiàn)社區(qū)結(jié)構(gòu)是目前復(fù)雜網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)研究領(lǐng)域的一個(gè)重要問(wèn)題。本文基于非負(fù)對(duì)稱(chēng)矩陣分解方法和標(biāo)簽擴(kuò)散方法,研究復(fù)雜網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)算法,提出一種節(jié)點(diǎn)影響力度量方法,并利用節(jié)點(diǎn)影響力,解決標(biāo)簽擴(kuò)散算法的標(biāo)簽選擇隨機(jī)性問(wèn)題。節(jié)點(diǎn)影響力度量方法包括節(jié)點(diǎn)相似度度量和節(jié)點(diǎn)重要性度量。利用非負(fù)對(duì)稱(chēng)矩陣分解可以獲取節(jié)點(diǎn)的隱因子特征向量,可以計(jì)算節(jié)點(diǎn)間的相似度。節(jié)點(diǎn)重要性由節(jié)點(diǎn)關(guān)聯(lián)的鄰居節(jié)點(diǎn)的數(shù)目度量。本文利用節(jié)點(diǎn)影響力,即基于節(jié)點(diǎn)相似度度量和節(jié)點(diǎn)重要性相結(jié)合的加權(quán)方式,提出基于非負(fù)對(duì)稱(chēng)矩陣分解加權(quán)的標(biāo)簽擴(kuò)散社區(qū)發(fā)現(xiàn)算法(MFWLP)。在MFWLP的基礎(chǔ)上,引入節(jié)點(diǎn)標(biāo)簽庫(kù)提出基于非負(fù)對(duì)稱(chēng)矩陣分解加權(quán)的重疊標(biāo)簽擴(kuò)散社區(qū)發(fā)現(xiàn)算法(OMFWLP)。本文提出的基于非負(fù)對(duì)稱(chēng)矩陣分解的非重疊社區(qū)發(fā)現(xiàn)算法(MFWLP)和重疊社區(qū)發(fā)現(xiàn)算法(OMFWLP)算法可在Hadoop分布式平臺(tái),采用Map-Reduce模型并行化實(shí)現(xiàn)。本文在多個(gè)真實(shí)網(wǎng)絡(luò)和人工合成網(wǎng)絡(luò)上進(jìn)行了實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,MFWLP能夠更準(zhǔn)確地發(fā)現(xiàn)非重疊社區(qū)結(jié)構(gòu),極大提高標(biāo)簽擴(kuò)散的穩(wěn)定性;OMFWLP也能夠更準(zhǔn)確地發(fā)現(xiàn)重疊社區(qū)結(jié)構(gòu)�；贖adoop并行化的MFWLP和OMFWLP能夠有效的對(duì)大規(guī)模網(wǎng)絡(luò)進(jìn)行社區(qū)發(fā)現(xiàn)。
[Abstract]:In a complex network, the nodes are divided into groups, the nodes in the group are closely connected and the connections between the groups are sparse. This characteristic is called the community structure of the complex network. In the large data age, the accurate discovery of the community structure, especially the accurate discovery of the community structure in the large-scale network, is the field of complex network community discovery research. An important problem. Based on the non negative symmetric matrix decomposition method and the label diffusion method, this paper studies the community discovery algorithm in complex networks, proposes a node influence measure method, and uses the node influence to solve the label selection randomness problem. The node influence measure includes the node similarity measure. And node importance measurement. Using non negative symmetric matrix decomposition can obtain the feature vector of the hidden factor of nodes, can calculate the similarity between nodes. The importance of nodes is measured by the number of neighbor nodes associated with nodes. This paper uses the node influence, which is based on the weight of node similarity measure and node importance. A label diffusion community discovery algorithm based on non negative symmetric matrix decomposition weighting (MFWLP) is proposed. On the basis of MFWLP, the node label library is introduced to propose a non negative symmetric matrix decomposition weighted community discovery algorithm (OMFWLP). The non overlapping community discovery algorithm based on the non negative symmetric matrix decomposition (MFWLP) and the non negative symmetric matrix decomposition algorithm (MFWLP) are proposed. The overlapping community discovery algorithm (OMFWLP) algorithm can be implemented in the Hadoop distributed platform with the Map-Reduce model. Experiments on multiple real networks and artificial networks have been carried out in this paper. The experimental results show that MFWLP can find non overlapping community structure more accurately, greatly improve the stability of the label diffusion; and OMFWLP can be more accurate. MFWLP and OMFWLP based on Hadoop parallelization can effectively detect community in large-scale networks.

【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：O157.5

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本文編號(hào)：1809858