本文關鍵詞：社交網(wǎng)絡中的信息傳播效應優(yōu)化方法研究　出處：《中國科學技術大學》2017年博士論文　論文類型：學位論文

  更多相關文章： 社交網(wǎng)絡 信息傳播 傳播效應 稠密子圖 信息覆蓋最大化 活躍度最大化

【摘要】：隨著互聯(lián)網(wǎng)技術的飛速發(fā)展,人們進入了連接一切的互聯(lián)網(wǎng)時代。作為新興崛起的一種互聯(lián)網(wǎng)應用,社交網(wǎng)絡滿足了人們固有的社交需求,成為了人們分享信息的重要平臺。社交網(wǎng)絡信息傳播的便捷性帶來巨大機遇的同時也帶來了前所未有的挑戰(zhàn)。一方面,社交網(wǎng)絡促進了社會交流、方便了民眾生活,催生了新的商業(yè)模式;另一方面,社交網(wǎng)絡也成了各種網(wǎng)絡輿情事件的滋生地。因此,深入探索社交網(wǎng)絡中的信息傳播規(guī)律對于輿情監(jiān)控、社會治理、商業(yè)變革都有重要意義�；谝陨媳尘�,本文針對傳統(tǒng)社交網(wǎng)絡信息傳播研究僅僅關注信息傳播本身的局限性,展開了社交網(wǎng)絡中的信息傳播效應優(yōu)化方法研究,分別從動態(tài)傳播過程、靜態(tài)網(wǎng)絡結構以及結合動態(tài)傳播過程和靜態(tài)網(wǎng)絡結構三個方面入手,研究了三個問題:信息傳播覆蓋最大化問題,稠密子圖統(tǒng)一挖掘框架問題以及信息傳播活躍度最大化問題。具體而言,本文主要的貢獻如下。首先,從信息傳播的動態(tài)過程出發(fā)研究了信息傳播覆蓋最大化問題。經典的影響力最大化問題只考慮信息傳播中的激活結點,忽略非激活結點可能的價值,而實際上非激活結點中包含了知曉信息的信息感知結點。要探索信息傳播產生的輻射效應,準確建模信息傳播覆蓋的范圍,就必須同時考慮信息的傳播者(激活結點)和信息的閱讀者(信息感知結點)。為此,本文提出了信息覆蓋最大化問題,該問題的目標函數(shù)在考慮了激活結點的數(shù)量的同時還考慮了信息感知結點的數(shù)量,因此能準確地度量信息傳播的覆蓋范圍。為了深入理解信息傳播的輻射效應,本文全面分析了信息覆蓋最大問題的性質,證明了該問題的計算復雜度,探索了目標函數(shù)的性質。在此基礎上,本文設計了貪心算法等三種不同的求解算法,并在三個真實數(shù)據(jù)集上驗證了算法的良好性能,同時證實了影響力最大化問題與信息覆蓋最大化問題的區(qū)別。最后,本文進一步探索了如何設定信息感知結點相對價值的問題,將信息覆蓋最大化進行了泛化與推廣。其次,從社交網(wǎng)絡的靜態(tài)結構出發(fā)研究了稠密子圖統(tǒng)一挖掘框架問題。社交網(wǎng)絡中的用戶往往以緊密連接的社團存在,可以說社團是社交網(wǎng)絡的骨架。對于單個用戶而言,不同的社團意味著不同的社交圈子,因此為了探索用戶的社交圈子,需要挖掘不同大小和密度的稠密子圖。相關研究雖然能挖掘特定大小與密度權衡下的稠密子圖,但是系統(tǒng)探索大小與密度權衡的統(tǒng)一框架仍是空白。為此,從二次規(guī)劃的角度,本文提出了稠密子圖的統(tǒng)一挖掘框架。該框架統(tǒng)一了已有的和本文新提出的目標函數(shù),可以系統(tǒng)地探索大小與密度之間的權衡。為了深入探索框架的性質,本文分別從數(shù)值優(yōu)化和圖論的角度對框架對應的優(yōu)化問題進行了分析,并擴展了收縮-擴展算法以求解框架對應的優(yōu)化問題。在四個數(shù)據(jù)集上,本文對提出的統(tǒng)一框架進行了實驗分析,實驗結果證實框架確實能挖掘不同大小與密度的子圖。最后,綜合考慮傳播的動態(tài)過程和靜態(tài)的網(wǎng)絡結構,研究了信息傳播活躍度最大化問題�；谟绊懥鞑サ膬�(yōu)化問題都是以"點"的視角看待問題的,把網(wǎng)絡中的結點當成孤立的個體來對待,忽略了這些結點之間的聯(lián)系。然而,結點之間存在復雜的網(wǎng)絡結構,在信息傳播過程中可能產生各種交互活動。因此,要探索信息傳播產生的交互效應,準確建模信息在網(wǎng)絡中的活躍度,就必須切換視角,從"邊"的視角去看待網(wǎng)絡中的激活結點,將他們當成一個相互之間有緊密聯(lián)系和交互的整體來看待。為此,本文提出了活躍度最大化問題,該問題以傳播導出子圖為建模目標,優(yōu)化傳播導出子圖上的交互強度總和,因此能準確刻畫信息在網(wǎng)絡中的活躍度。為了深入理解信息傳播的交互效應,本文全面分析活躍度最大化問題的性質,證明了該問題的計算復雜度,討論了該問題的可近似性,建立了該問題與最稠密子圖發(fā)現(xiàn)問題之間的聯(lián)系,并探索了目標函數(shù)的性質。為了求解該問題,本文為目標函數(shù)分別設計了上界和下界,并分析了上界和下界的優(yōu)化性質。在此基礎上,本文提出了活躍度及其上下界的無偏估計,然后設計并高效實現(xiàn)了一種基于采樣的算法,并得到了該算法的一個依賴于數(shù)據(jù)的近似因子。在兩個真實數(shù)據(jù)集和兩個合成數(shù)據(jù)集上,本文驗證了提出的算法的性能和近似因子,并分析了影響力和活躍度之間的關系。
[Abstract]:With the rapid development of Internet technology, people have entered into the connection of all Internet era. As a kind of emerging Internet applications, social network to meet the people's inherent social needs, has become an important platform for people to share information. The convenience of the social network information dissemination has brought great opportunities but also brought hitherto unknown challenge. On the one hand, the social network promotes social communication, facilitate the people's lives, the birth of a new business model; on the other hand, the social network has become a breeding ground for a variety of network public opinion events. Therefore, deep into the exploration of information propagation in social networks for monitoring public opinion, social governance, has important significance for business transformation. Based on the above background, this paper studies the traditional social network information dissemination only focus on the limitations of the dissemination of information itself, the dissemination of information in social network Research on the optimization method of effect, from the dynamic propagation process, static network structure and communication process combined with dynamic and static network structure of the three aspects of three issues: the problem of maximizing the coverage of information dissemination, dense subgraph mining framework and unified information dissemination activity maximization problem. Specifically, the main contribution of this paper as follows. First, research on the dissemination of information covering the maximization problem starting from the dynamic process of information dissemination. The influence maximization problem classic only consider the active nodes in the dissemination of information, ignoring the non value node may be activated, but actually non active nodes contain information perception node information. To explore the knowledge radiation effect of information the scope of information dissemination, accurate modeling coverage, we must also consider the disseminator of information (active nodes) and information reader (letter Information sensing node). For this reason, this paper presents an information coverage maximization problem, the objective function of the problem in consideration of the number of active nodes also consider the number of sensing nodes, so it can accurately measure the coverage of information dissemination. In order to understand the effect of information dissemination, this paper makes a comprehensive analysis the nature of information covering the biggest problem, proved that the computational complexity of the problem, explore the nature of the objective function. On this basis, this paper design a greedy algorithm with three different algorithms, and in three real data sets show the excellent performance of the algorithm, and confirm the difference of influence maximization the maximum problem and information coverage. Finally, this paper further explores how to set the node information perception of relative value, to cover the information maximization of generalization and promotion. Secondly, Based on the static structure of the social network of the dense subgraph mining framework. The unified social network users often exist in close connection with society, can be said that the association is a social network framework. For individual users, different society means the social circle is different, so in order to explore the user's social circle, need the size and density of different mining dense sub graph mining. Although related research to the size and density of dense sub specific balance under the map, but the system of unified framework is still blank size and density balance. Therefore, two from the planning point of view, this paper proposes a unified dense Subgraph Mining Framework. The framework of a unified objective function the existing and proposed in this paper, systematically explores the trade-off between size and density. In order to explore the properties of the framework, this paper from the numerical optimization and graph theory The angle of the corresponding optimization problem framework are analyzed, and expanded the contraction expansion algorithm to solve the optimization problem. The corresponding framework on four data sets, this paper carries on the experimental analysis framework proposed, experimental results show that the framework can tap the different size and density of the sub graph. Finally, considering the the network structure of dynamic and static communication, research information dissemination activity maximization problem. The optimization problem of influence propagation are based on the view of "points" from the perspective of the network as a node in the isolated to treat, ignoring the links between these nodes. However, the complexity of the network structure the presence of nodes in the information dissemination process may produce a variety of interactive activities. Therefore, to explore the interactive effects of information dissemination of accurate information in network modeling activity, must be cut Change the perspective to look at active nodes in the network from the perspective of "edge", they will be as a close contact and interaction between the whole. Therefore, the proposed activity maximization problem, the problem is to spread the induced subgraph for modeling, optimization of transmission interaction strength sum induced subgraph on the map, so it can accurately describe the information in the network activity. In order to understand the interaction of information dissemination, this paper makes a comprehensive analysis of the nature of the activity maximization problem, proved that the computational complexity of the problem, discussed the approximability of the problem, the problem is established and the most dense sub graph links between problems, and explores the nature of the objective function. In order to solve this problem, this paper designed the objective function of the upper and lower bounds, and analyzes the properties of the upper and lower bounds on the optimization. Based on this, proposed to live Jump of the lower and upper bounds of unbiased estimation, then design and efficient implementation of a sampling based algorithm, and get a dependence on the algorithm for data approximation factor. In two real data sets and two synthetic data sets, this paper verified the performance of the proposed algorithm and the approximation factor. And analyzes the relationship between influence and activity.

【學位授予單位】：中國科學技術大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP393.09;G206

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