本文選題：社交網(wǎng)絡(luò)　切入點(diǎn)：社區(qū)結(jié)構(gòu)　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：社交網(wǎng)絡(luò)影響力分析已經(jīng)成為社交網(wǎng)絡(luò)分析的重要方面,而其中的社交網(wǎng)絡(luò)影響最大化問題也受到越來越多的關(guān)注,特別是Web 2.0時(shí)代下在線社交網(wǎng)絡(luò)平臺(tái)的興起為影響最大化問題的研究提供了更加豐富的平臺(tái)和研究數(shù)據(jù)。社交網(wǎng)絡(luò)影響最大化問題是研究如何在社交網(wǎng)絡(luò)中選擇有限個(gè)數(shù)的影響力節(jié)點(diǎn)而使影響力傳播達(dá)到最大。這個(gè)問題的研究對(duì)病毒式營銷,推薦系統(tǒng)和突發(fā)事件檢測等領(lǐng)域有重要意義。近年來,越來越多的影響最大化方法被提了出來,這些算法大致可以分為三類:針對(duì)貪婪算法的改進(jìn)算法,基于社交網(wǎng)絡(luò)社區(qū)結(jié)構(gòu)特性的算法,基于社交網(wǎng)絡(luò)節(jié)點(diǎn)特性的啟發(fā)式方法。其中,針對(duì)貪婪算法的改進(jìn)算法可以提升貪婪算法的效率,但仍不適合用于大規(guī)模社交網(wǎng)絡(luò)中;基于社交網(wǎng)絡(luò)社區(qū)結(jié)構(gòu)特性的算法可以獲得較好的效果和效率;基于社交網(wǎng)絡(luò)節(jié)點(diǎn)特性的啟發(fā)式方法的效率最高,但其得到的影響力節(jié)點(diǎn)的效果卻不好。Memetic算法是近年來進(jìn)化計(jì)算領(lǐng)域的一個(gè)研究熱點(diǎn),它是一種基于群體的全局搜索和基于個(gè)體的局部搜索的結(jié)合體,可以彌補(bǔ)這兩者單方面的不足,獲得較快的搜索效率和令人滿意的結(jié)果。本文利用社區(qū)結(jié)構(gòu)和Memetic算法的優(yōu)點(diǎn),將其應(yīng)用于社交網(wǎng)絡(luò)影響最大化問題中。本文所做的主要工作如下:(1)研究了社交網(wǎng)絡(luò)中節(jié)點(diǎn)之間的結(jié)構(gòu)相似性對(duì)社交網(wǎng)絡(luò)影響最大化問題的影響,提出了基于節(jié)點(diǎn)相似性的度中心性方法。在該方法中,我們用節(jié)點(diǎn)之間的結(jié)構(gòu)相似性來排除與度大的節(jié)點(diǎn)相似的節(jié)點(diǎn),以此來減少影響力節(jié)點(diǎn)之間影響力傳播的重疊,從而獲得較大的影響力傳播。(2)研究了Memetic算法在社交網(wǎng)絡(luò)影響最大化問題上的應(yīng)用。我們提出了基于Memetic算法的影響最大化問題,將局部搜索策略加入Memetic算法中,改善了傳統(tǒng)遺傳算法收斂速度慢,容易陷入局部最優(yōu)的缺點(diǎn)。同時(shí),該算法與社交網(wǎng)絡(luò)的社區(qū)結(jié)構(gòu)特性結(jié)合,縮小了影響力節(jié)點(diǎn)的搜索空間,改善了網(wǎng)絡(luò)規(guī)模大算法搜索空間大的缺點(diǎn),加快了算法的收斂。(3)我們針對(duì)前面兩方面的工作分別在三個(gè)不同規(guī)模的真實(shí)網(wǎng)絡(luò)上進(jìn)行實(shí)驗(yàn)。針對(duì)第一個(gè)工作,我們研究了節(jié)點(diǎn)相似性對(duì)不同網(wǎng)絡(luò)的影響和基于節(jié)點(diǎn)相似性方法對(duì)影響最大化問題的有效性。針對(duì)第二個(gè)工作,我們首先對(duì)社區(qū)結(jié)構(gòu)和局部搜索策略的有效性進(jìn)行實(shí)驗(yàn),然后對(duì)提出的算法的效果和效率進(jìn)行實(shí)驗(yàn)。
[Abstract]:Social network impact analysis has become an important aspect of social network analysis, and the problem of maximizing the impact of social network has attracted more and more attention. In particular, the rise of online social networking platforms in the era of Web 2.0 provides a richer platform and research data for the study of impact maximization. The problem of maximizing the impact of social networks is to study how to choose among them. A limited number of influence nodes to maximize the spread of influence. In recent years, more and more impact maximization methods have been proposed. These algorithms can be divided into three categories: improved algorithms for greedy algorithms, The algorithm based on the community structure characteristic of social network and the heuristic method based on the characteristic of social network node, among them, the improved algorithm for greedy algorithm can improve the efficiency of greedy algorithm, but it is still not suitable for large-scale social network. The algorithm based on the characteristics of social network community structure can obtain better effect and efficiency, and the heuristic method based on the characteristics of social network nodes is the most efficient. However, the effect of its influence nodes is not good. Memetic algorithm is a hot topic in the field of evolutionary computing in recent years. It is a combination of global search based on population and local search based on individual. It can make up for the shortcomings of these two methods and obtain fast search efficiency and satisfactory results. This paper makes use of the advantages of community structure and Memetic algorithm. The main work of this paper is as follows: 1) the influence of structural similarity between nodes in social network on the problem of maximizing the influence of social network is studied. In this method, the structural similarity between nodes is used to eliminate nodes similar to large nodes, so as to reduce the overlap of influence propagation between influential nodes. In this paper, we study the application of Memetic algorithm in the problem of maximizing the influence of social networks. We propose the problem of maximizing the influence based on Memetic algorithm and add the local search strategy to the Memetic algorithm. The traditional genetic algorithm has the disadvantage of slow convergence and easy to fall into local optimum. At the same time, the algorithm combines with the community structure characteristics of social network, and reduces the search space of the influential nodes. It improves the shortcoming of large network size algorithm search space, and accelerates the convergence of the algorithm.) We do experiments on three real networks of different scales in view of the first two aspects of the work. For the first work, we do experiments on three real networks of different scales. We study the effect of node similarity on different networks and the effectiveness of node similarity based approach for maximizing impact. In the second work, we first test the effectiveness of community structure and local search strategy. Then the effect and efficiency of the proposed algorithm are tested.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP393.09

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