本文關(guān)鍵詞： 最短路徑 中介系數(shù) 相似度 凝聚系數(shù) 特征屬性 自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)　出處：《江西理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)技術(shù)的迅猛發(fā)展,收集并處理規(guī)模龐大且種類繁多的實(shí)際網(wǎng)絡(luò)數(shù)據(jù)成為滿足物質(zhì)與文化需求的必要途徑,網(wǎng)絡(luò)科學(xué)也隨之扮演著愈來(lái)愈重要的角色。與人們生活緊密相關(guān)的網(wǎng)絡(luò),如社會(huì)網(wǎng),生物網(wǎng),信息網(wǎng),交通運(yùn)輸網(wǎng)等,這些網(wǎng)絡(luò)之間相互交錯(cuò)關(guān)聯(lián)。揭示網(wǎng)絡(luò)中共性的問(wèn)題以及解決這些問(wèn)題的普適方法便成為了網(wǎng)絡(luò)研究的一個(gè)重點(diǎn),而這些網(wǎng)絡(luò)可以歸納于復(fù)雜網(wǎng)絡(luò)的范疇。挖掘出其中隱藏的社團(tuán)結(jié)構(gòu),對(duì)病毒傳播的預(yù)防、輿情的控制、以及未知生物功能的預(yù)測(cè)均起到至關(guān)重要的作用。本文針對(duì)復(fù)雜網(wǎng)絡(luò)中社團(tuán)結(jié)構(gòu)的挖掘所做工作如下:(1)綜述了復(fù)雜網(wǎng)絡(luò)的研究現(xiàn)狀、相關(guān)定義、性質(zhì)及模型,分析了社團(tuán)結(jié)構(gòu)的層次劃分,敘述了研究社團(tuán)結(jié)構(gòu)的意義,總結(jié)了典型的社團(tuán)結(jié)構(gòu)劃分算法的優(yōu)缺點(diǎn),論述了利用聚類的算法思想以及自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)(簡(jiǎn)稱SOM)的相關(guān)知識(shí)對(duì)社團(tuán)結(jié)構(gòu)進(jìn)行挖掘。(2)提出了基于最短路徑特征的社團(tuán)挖掘算法(Community Discovery Algorithm Based on Shortest Path Feature,SPCDA)�；谧疃搪窂降奶卣�,由其數(shù)目的特征計(jì)算每個(gè)節(jié)點(diǎn)的中介系數(shù)從而獲取社團(tuán)中心,據(jù)其長(zhǎng)度的特征計(jì)算節(jié)點(diǎn)之間的相似度值。約定一種閾值作為劃分規(guī)則,該閾值最終由所有節(jié)點(diǎn)的平均相似度值確定。如此以來(lái)構(gòu)成類似于聚類的模型,最后按照劃分規(guī)則將每個(gè)節(jié)點(diǎn)(不包括社團(tuán)中心的節(jié)點(diǎn))分別與閾值進(jìn)行比較,取超過(guò)閾值的節(jié)點(diǎn)劃分聚類,據(jù)此過(guò)程不斷迭代,直至劃分完成。將該算法應(yīng)用于經(jīng)典的復(fù)雜網(wǎng)絡(luò)實(shí)驗(yàn)仿真平臺(tái),并與典型的GN算法和LPA算法進(jìn)行比較分析,結(jié)果證實(shí)SPCDA算法能夠快速、準(zhǔn)確的挖掘隱藏的社團(tuán)結(jié)構(gòu)。(3)提出了基于自組織競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)的多特征社團(tuán)挖掘算法(Multi-Feature Community Discovery Algorithm Based on Self-Organizing Competitive Neural Network,SOMCD A)�？紤]網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)兼顧節(jié)點(diǎn)特征屬性,將聚類思想與SOM相結(jié)合。提出的算法基于節(jié)點(diǎn)的影響力,結(jié)合節(jié)點(diǎn)的度及其相鄰節(jié)點(diǎn)之間的連接邊數(shù)來(lái)計(jì)算每個(gè)節(jié)點(diǎn)的凝聚系數(shù),從凝聚系數(shù)值較大的節(jié)點(diǎn)中提取出特征節(jié)點(diǎn),并將這些有代表性的特征節(jié)點(diǎn)作為樣本節(jié)點(diǎn)。然后針對(duì)樣本節(jié)點(diǎn)的多特征屬性信息用SOM對(duì)其進(jìn)行訓(xùn)練,再將非樣本節(jié)點(diǎn)提供給經(jīng)過(guò)訓(xùn)練的SOM。依據(jù)SOM的結(jié)構(gòu)存儲(chǔ)模式的特征,競(jìng)爭(zhēng)網(wǎng)絡(luò)就會(huì)做出識(shí)別,從而實(shí)現(xiàn)社團(tuán)劃分的目的。最后根據(jù)每次仿真所取的競(jìng)爭(zhēng)層神經(jīng)元個(gè)數(shù)的不同,采用模塊度函數(shù)來(lái)確定最佳社團(tuán)結(jié)構(gòu)。
[Abstract]:With the rapid development of computer technology, the collection and processing of a large and diverse range of actual network data has become a necessary way to meet the material and cultural needs. Network science also plays a more and more important role. Networks closely related to people's lives, such as social networks, biological networks, information networks, transportation networks and so on. These networks are interlaced with each other. Revealing the common problems in the network and the universal methods to solve these problems have become a focus of the network research. These networks can be summed up in the category of complex networks, mining out the hidden community structure, the prevention of virus transmission, the control of public opinion. The prediction of unknown biological functions plays an important role. In this paper, the research status and definitions of complex networks are summarized as follows: 1) for the mining of community structures in complex networks. Properties and models, analysis of the hierarchy of community structure, the significance of the study of community structure, summed up the advantages and disadvantages of typical community structure division algorithm. This paper discusses how to mine the community structure by using the idea of clustering and the knowledge of self-organizing competitive neural network (SOM). The algorithm based on the shortest path feature is proposed. Community Discovery Algorithm Based on Shortest Path Feature. Based on the features of the shortest path, the mediation coefficient of each node is calculated from the number of features to obtain the community center. The similarity value between nodes is calculated according to its length feature. A threshold value is agreed as a partition rule, which is determined by the average similarity value of all nodes. Thus, a clustering model is constructed. Finally, each node (not including the node in the community center) is compared with the threshold value according to the partition rule, and the node that exceeds the threshold value is taken to divide and cluster, according to which the process is iterated. The algorithm is applied to the classical simulation platform of complex network experiments and compared with the typical GN algorithm and LPA algorithm. The results show that the SPCDA algorithm can be fast. In this paper, we propose a multi-feature association mining algorithm based on self-organizing competitive neural network. Multi-Feature Community Discovery Algorithm Based on Self-Organizing. Competitive Neural Network. Considering the topological structure of the network and the characteristic attributes of the nodes, the clustering idea is combined with the SOM. The proposed algorithm is based on the influence of the nodes. The coacervation coefficient of each node is calculated by combining the degree of nodes and the number of connecting edges between adjacent nodes, and the feature nodes are extracted from the nodes with larger coacervation coefficient. These representative feature nodes are taken as sample nodes, and then the multi-feature attribute information of the sample nodes is trained with SOM. Then the non-sample nodes are provided to the trained SOM. According to the characteristics of the structural storage mode of SOM, the competitive network will be identified. Finally, according to the different number of competition layer neurons in each simulation, the module degree function is used to determine the optimal community structure.
【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】：2017
【分類號(hào)】：O157.5;TP301.6

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