社交网络影响力传播的分析与挖掘研究
发布时间：2019-04-20 14:30
【摘要】：社交网络已经成为大众获取信息和交流信息的重要媒介。影响力传播是社交网络的重要特征之一。对社交网络的影响力传播展开分析与挖掘研究有利于信息扩散、商品营销、广告投放、以及舆情管控等应用的实施。然而,随着社交网络规模的不断增大,各种应用要求的不断提高,以及不同用户对信息传播受益者的重要性具有差异性等因素的存在,现有研究仍难以满足用户的应用需求。为此,本文在分析和总结已有工作不足的基础上,利用社交网络中用户的行为信息以及社会关系信息等,针对社交网络中影响力传播面临的问题展开研究。具体研究成果包括： 第一,针对用户间的影响力度量问题,本文根据影响力在传播中具有的累积特性,以线性阈值模型(Linear Threshold Model,简称为LT模型)为基础,提出一种基于LT模型的用户间影响力度量方法。该方法首先利用最大熵原理,估计用户激活阈值的概率密度函数,并据此计算用户被激活的概率。然后以社交网络中用户的历史行为日志为样本,借鉴最大似然估计思想将用户间影响力度量问题建模为一个满足约束条件的优化问题；并根据问题目标函数和约束的特点,设计相应的求解算法。该算法以粒子群方法为基础,通过问题映射、适应度函数建立、越界阻止、动态参数设置和最优粒子变异等优化策略,有效地学习用户间影响力。最后,实验以真实的社交网络数据和相关用户的历史行为日志为分析对象,验证了所提方法的有效性。 第二,针对社交网络的影响力传播最大化问题,本文提出一种基于兴趣社区划分的影响最大化方法。该方法以用户的历史行为信息及社会关系信息为基础,提取用户行为相似性和社会关系相似性来共同度量用户兴趣之间的相似性；其次,根据用户兴趣之间的相似度,使用NCUT算法将社交网络划分成若干个兴趣社区。然后,采用贪婪策略,根据动态过滤边界的取值快速从兴趣社区中选择影响力边际效益最大的节点作为初始种子用户。最后,实验结果表明,该方法能够在保证影响效果的同时,有效提高该问题的求解效率。 第三,针对面向特定用户的影响最大化问题,本文在影响力传播模型的基础上对该问题进行建模,并给出相应求解方法。首先,在问题建模时,由于影响力传播具有不确定性,本文基于影响力传播模型,设计随机函数来模拟问题的目标。该函数根据其他用户对特定用户邻居的激活情况以及特定用户邻居对其的影响力取值,分两段完成其他用户对特定用户的影响力计算。其次,在问题求解时,本文根据问题目标函数的子模特性,采用贪婪策略设计了一种具有精度保证为63%的近似求解算法；特别地,针对大规模的社交网络,本文还设计了相应的快速启发式求解算法。最后,实验以真实的社交网络数据集为仿真对象验证了设计方法的有效性。
[Abstract]:Social networks have become an important medium for the public to access and exchange information. Influence communication is one of the important characteristics of social networks. The analysis and mining of social network influence dissemination is beneficial to the application of information diffusion, commodity marketing, advertising, public opinion control and so on. However, with the increasing scale of social networks, the increasing requirements of various applications, and the difference in the importance of different users to the beneficiaries of information dissemination, the existing research is still difficult to meet the application needs of users. Therefore, on the basis of analyzing and summarizing the shortage of the existing work, this paper makes use of the behavior information of the users and the information of the social relations in the social network to study the problems of the influence communication in the social network. The specific results are as follows: firstly, aiming at the measurement of influence power among users, according to the cumulative characteristics of influence in propagation, this paper is based on the linear threshold model (Linear Threshold Model,), which is called the LT model (abbreviated as the linear threshold model for short). A method based on LT model for measurement of user-to-user influence power is proposed in this paper. In this method, the probability density function of user activation threshold is estimated by using the maximum entropy principle, and the probability of user activation is calculated. Then the user's historical behavior log is taken as a sample and the maximum likelihood estimation (MLE) is used as a reference to model the inter-user impact capacity measurement problem as an optimization problem which satisfies the constraint conditions. According to the characteristics of objective function and constraint, the corresponding algorithm is designed. Based on Particle Swarm Optimization (PSO) algorithm, this algorithm can effectively learn the influence between users through the optimization strategies such as problem mapping, establishment of adaptability function, cross-boundary blocking, dynamic parameter setting and optimal particle mutation. Finally, the real social network data and users' historical behavior log are analyzed to verify the effectiveness of the proposed method. Secondly, in order to maximize the influence propagation of social networks, this paper proposes an influence maximization method based on community of interest division. Based on the information of user's historical behavior and social relationship, this method extracts the similarity of user's behavior and social relation to measure the similarity of user's interest together. Secondly, according to the similarity between users' interests, the NCUT algorithm is used to divide the social network into several interest communities. Then, according to the value of dynamic filtering boundary, greedy strategy is adopted to quickly select the node with the greatest marginal benefit in interest community as the initial seed user. Finally, the experimental results show that the proposed method can effectively improve the efficiency of solving the problem while ensuring the effect of the problem. Thirdly, aiming at the user-oriented influence maximization problem, this paper models the problem on the basis of the influence propagation model, and gives the corresponding solution method. Firstly, when modeling the problem, because of the uncertainty of influence propagation, this paper designs a random function to simulate the goal of the problem based on the influence propagation model. The function calculates the influence of other users on a particular user in two segments according to the activation of other users to a particular user's neighbor and the influence of a particular user's neighbor on it. Secondly, according to the sub-module characteristic of the objective function of the problem, this paper designs an approximate solution algorithm with 63% precision by using greedy strategy. In particular, for large-scale social networks, this paper also designs a fast heuristic algorithm. Finally, the real social network data set is used as the simulation object to verify the effectiveness of the design method.
【学位授予单位】：北京邮电大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：G206
[Abstract]:Social networks have become an important medium for the public to access and exchange information. Influence communication is one of the important characteristics of social networks. The analysis and mining of social network influence dissemination is beneficial to the application of information diffusion, commodity marketing, advertising, public opinion control and so on. However, with the increasing scale of social networks, the increasing requirements of various applications, and the difference in the importance of different users to the beneficiaries of information dissemination, the existing research is still difficult to meet the application needs of users. Therefore, on the basis of analyzing and summarizing the shortage of the existing work, this paper makes use of the behavior information of the users and the information of the social relations in the social network to study the problems of the influence communication in the social network. The specific results are as follows: firstly, aiming at the measurement of influence power among users, according to the cumulative characteristics of influence in propagation, this paper is based on the linear threshold model (Linear Threshold Model,), which is called the LT model (abbreviated as the linear threshold model for short). A method based on LT model for measurement of user-to-user influence power is proposed in this paper. In this method, the probability density function of user activation threshold is estimated by using the maximum entropy principle, and the probability of user activation is calculated. Then the user's historical behavior log is taken as a sample and the maximum likelihood estimation (MLE) is used as a reference to model the inter-user impact capacity measurement problem as an optimization problem which satisfies the constraint conditions. According to the characteristics of objective function and constraint, the corresponding algorithm is designed. Based on Particle Swarm Optimization (PSO) algorithm, this algorithm can effectively learn the influence between users through the optimization strategies such as problem mapping, establishment of adaptability function, cross-boundary blocking, dynamic parameter setting and optimal particle mutation. Finally, the real social network data and users' historical behavior log are analyzed to verify the effectiveness of the proposed method. Secondly, in order to maximize the influence propagation of social networks, this paper proposes an influence maximization method based on community of interest division. Based on the information of user's historical behavior and social relationship, this method extracts the similarity of user's behavior and social relation to measure the similarity of user's interest together. Secondly, according to the similarity between users' interests, the NCUT algorithm is used to divide the social network into several interest communities. Then, according to the value of dynamic filtering boundary, greedy strategy is adopted to quickly select the node with the greatest marginal benefit in interest community as the initial seed user. Finally, the experimental results show that the proposed method can effectively improve the efficiency of solving the problem while ensuring the effect of the problem. Thirdly, aiming at the user-oriented influence maximization problem, this paper models the problem on the basis of the influence propagation model, and gives the corresponding solution method. Firstly, when modeling the problem, because of the uncertainty of influence propagation, this paper designs a random function to simulate the goal of the problem based on the influence propagation model. The function calculates the influence of other users on a particular user in two segments according to the activation of other users to a particular user's neighbor and the influence of a particular user's neighbor on it. Secondly, according to the sub-module characteristic of the objective function of the problem, this paper designs an approximate solution algorithm with 63% precision by using greedy strategy. In particular, for large-scale social networks, this paper also designs a fast heuristic algorithm. Finally, the real social network data set is used as the simulation object to verify the effectiveness of the design method.
【学位授予单位】：北京邮电大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：G206
【参考文献】
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1 冀进朝;韩笑;王U，
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