【摘要】：社會(huì)網(wǎng)絡(luò)從根本上說(shuō)是一種復(fù)雜網(wǎng)絡(luò),是復(fù)雜網(wǎng)絡(luò)在現(xiàn)實(shí)世界中的應(yīng)用。社會(huì)網(wǎng)絡(luò)是個(gè)體與不同社會(huì)關(guān)系的集合,反映了社會(huì)個(gè)體之間的相互活動(dòng)。社會(huì)影響力分析是社會(huì)網(wǎng)絡(luò)的重要研究方向,其中個(gè)體的影響力和重要性的度量是一個(gè)熱點(diǎn)問(wèn)題,在抑制疾病傳播和商品營(yíng)銷相關(guān)領(lǐng)域有著重要作用。目前已經(jīng)有多種算法被提出,用來(lái)分析節(jié)點(diǎn)自身屬性和特點(diǎn),衡量個(gè)體在網(wǎng)絡(luò)中的重要程度,本文主要研究社會(huì)網(wǎng)絡(luò)中重要節(jié)點(diǎn)挖掘問(wèn)題�？紤]到已有的節(jié)點(diǎn)影響力和重要性度量算法大多只關(guān)注了節(jié)點(diǎn)的自身屬性,顯然節(jié)點(diǎn)的重要性不僅與其自身的局部屬性相關(guān),也與節(jié)點(diǎn)所在網(wǎng)絡(luò)中的全局特性相關(guān)�；诖吮疚奶岢隽艘环N基于雙尺度的重要節(jié)點(diǎn)挖掘算法(KShell and Local Degree Centrality,KSLD),結(jié)合了節(jié)點(diǎn)的局部信息和全局信息。本文首先利用KShell分解對(duì)網(wǎng)絡(luò)層次進(jìn)行了劃分并使用熵衡量節(jié)點(diǎn)影響全局網(wǎng)絡(luò)層的能力,接著在局部信息的獲取上由于傳統(tǒng)中心性算法各有優(yōu)劣,本文對(duì)傳統(tǒng)算法的效率和效果做出平衡和改進(jìn),提出局部度中心性的算法計(jì)算節(jié)點(diǎn)局部信息,最后的實(shí)驗(yàn)對(duì)于局部度算法的效果與KSLD的應(yīng)用分別做了論證和對(duì)比,并證明了結(jié)合了雙尺度的KSLD算法對(duì)不同類型的網(wǎng)絡(luò)的適用性比傳統(tǒng)算法要好。目前大多數(shù)節(jié)點(diǎn)影響力的分析都是以靜態(tài)網(wǎng)絡(luò)為基礎(chǔ)進(jìn)行,而現(xiàn)實(shí)中網(wǎng)絡(luò)是不斷演變的,所以靜態(tài)網(wǎng)絡(luò)基礎(chǔ)上的節(jié)點(diǎn)影響力特征并不能很好的適用于實(shí)際網(wǎng)絡(luò)狀況,針對(duì)這一問(wèn)題,本文將個(gè)體影響力分析深入到演化網(wǎng)絡(luò)中來(lái),研究了演化網(wǎng)絡(luò)中的信息傳播特征,分析節(jié)點(diǎn)的影響力擴(kuò)散方式,發(fā)現(xiàn)節(jié)點(diǎn)在網(wǎng)絡(luò)演化中不斷離開(kāi)社區(qū)和加入新社區(qū)的事件屬性,本文對(duì)于事件進(jìn)行了詳細(xì)的定義,并結(jié)合這一特點(diǎn)給出了一種基于事件的節(jié)點(diǎn)影響力評(píng)價(jià)方法(Based on Event Centrality,BE),通過(guò)社交指數(shù)和影響力指數(shù)兩個(gè)指標(biāo)對(duì)節(jié)點(diǎn)影響力進(jìn)行了評(píng)估和排序找出影響力較大的重要節(jié)點(diǎn),最后通過(guò)實(shí)驗(yàn)驗(yàn)證了該方法的可行性。
[Abstract]:Social network is a kind of complex network, which is the application of complex network in the real world. Social network is a collection of individual and different social relations, reflecting the interaction between social individuals. Social impact analysis is an important research direction of social network, in which the measurement of individual influence and importance is a hot issue, and plays an important role in the field of disease suppression and commodity marketing. At present, a variety of algorithms have been proposed to analyze the attributes and characteristics of nodes and to measure the importance of individuals in the network. This paper mainly studies the mining of important nodes in social networks. Considering that most of the existing algorithms only focus on the properties of nodes, it is obvious that the importance of nodes is not only related to their own local attributes, but also to the global characteristics of the network in which the nodes are located. Based on this, an important node mining algorithm, (KShell and Local Degree Centrality,KSLD, is proposed, which combines the local and global information of nodes. This paper first uses KShell decomposition to divide the network layer and uses entropy to measure the ability of nodes to influence the global network layer. Then the traditional centrality algorithm has its own advantages and disadvantages in obtaining local information. In this paper, the efficiency and effect of the traditional algorithm are balanced and improved, and the local degree centrality algorithm is proposed to calculate the local information of nodes. Finally, the effect of the local degree algorithm is proved and compared with the application of KSLD. It is proved that the two-scale KSLD algorithm is more suitable for different types of networks than the traditional algorithm. At present, most of the analysis of node influence is based on static network, but in reality the network is constantly evolving, so the characteristics of node influence based on static network can not be well applied to the actual network situation. In order to solve this problem, this paper studies the characteristics of information dissemination in evolutionary networks, and analyzes the influence diffusion mode of nodes. In this paper, the event attribute of node leaving community and joining new community is found in the evolution of network. In this paper, the event is defined in detail, and an event based node impact evaluation method, (Based on Event Centrality, is given. BE), evaluates the influence of nodes by social index and influence index, and sorts out the important nodes with great influence. Finally, the feasibility of this method is verified by experiments.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5

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