|Thesis Advisor||胡卫明 ; 吴偶|
|Place of Conferral||北京|
|Keyword||社会影响力 文本交互 融合算法 信任（关系）预测 影响力最大|
With the Internet growing prosperously, the long distance no longer hinders the people's communication and information sharing. As a result, many kinds of web communities based on different regions, interests and ideas are booming and play a more and more significant role in effective information diffusion. The "virtual" web communities largely extend the concept of offline communities, which make the information flow much faster and wider than before. In communication and interaction between people in a community, a kind of force that is named social influences naturally generates, which affects, controls or operates somebody or something. The object under the force may change his mind or make a decision to behave similarly to others in the same community. Finally, the people in the whole community are consistent with behavioral tendency or emotional experience. Social influence analysis is an important research area in the principle of social computing, which study the mechanism of formation and rule of development for this consistent behavior among people in the same community. The traditional methods for social influence analysis oriented to the offline communities largely come from social subjects such as social psychology, social cognition or marketing, which use questionnaire to collect the essential data sets, and some basic statistical algorithms (e.g. regression or factor analysis) to analyze the data sets, yet some basic conclusions are drawn. The conclusions from the investigation using traditional methods have shown a great value in marketing, advertising, public policy and many other areas in the past decades. As the virtual web communities are quickly developed in recent years, there are increasing successful business cases in social marketing, word-of-mouth advertising with respect to the large online social networks (e.g. Facebook, Twitter, Weibo). Thus, the social influence analysis for online web communities increasingly becomes a significant research direction in web mining and social computing.
This thesis mainly focus on the area of large-scale social data processing and social computing, especially discovering the principles of social influence and its diffusion in web communities. The influence comes out as the web users interact with each other in a web community, where there are many kinds of interaction Medias. The thesis mostly concentrates the text-based interaction which is still the most convenient and effective interaction media on the web. Given that the interaction data in social networks almost all exist the following problems: highly-fragmented, noisy or lack of background knowledge, different from the traditional method in knowledge engineering which should create a background domain knowledge base with much difficulty, we advocate learning from data, fully recognizing the diversity of the social-interaction text data. Given the special structure of the web community, we emphasize the fusion strategies between the text-based interaction from the web users and the structure of the web community, which tries to answer the questions that how the users are influenced by the text-based interaction, and who are influenced by the others. Besides, the thesis introduces a simple yet effective strategy to solve the trust prediction in signed social networks with friend and foe relationships. The strategy introduces here motivated by the previous conclusions from social psychology. The trust prediction problem is the cornerstone of influence diffusion, same as the previous fusion work, which answer the questions that how the users are influenced and who are influenced. At the end, the thesis introduces an influence maximization problem in human-intervened social networks, and analyzes the influence diffusion in constraint conditions, which is to answer the question that how wide the influence can diffuse given the limited budget and time. The main contributions of this thesis are summarized as follows:
(1) A unified fusion framework for the time-related rank model is proposed to handle the post rank with respect to its influence in the web community. The aim of the framework is to fuse the semantic information of the text-based interaction into the special structure of the community. The basic ideas are carried out as follows. First, the interaction text are represented in different semantic scales. With the assumption of the consistency of the domain knowledge in a web community, different semantic quality measures are learned. Accordingly, different semantic trees are reconstructed based on the semantic similarity measures in different semantic scales. The time-related rank values of the nodes in different semantic trees can then be easily calculated. Second, the rank values are fused based on the semantic quality measures learned previously. If the community reply-to structure is explicitly given, the semantics and structure can also be fused in the same unified fusion framework.
(2) A metadata based clustered multi-task learning method is proposed to conduct to the influence prediction problem in web communities with text-based interactions. The purpose of the method is to take the reply-to structure of web communities as the web context for the text-based interactions. With the social metadata splitting in different clusters, the multiple learning tasks are naturally created. By the first dividing then learning strategy, the clustered multi-task learning method can solve the two commonly existing problems in handling the web data. The one problem is that the dimension of the web data is too high, which easily causes the over-fitting problem if we want to learn a unified model. The other problem is that the learning tasks are too much if we handle them as different independent learning tasks, which causes the under-fitting problem because the samples in each task are inefficient. In one word, the substantive problem is that the interaction data in web communities are the highly fragmented and lack of semantics. As a result, A compromise is found to conduct the ``divide and learn'' strategy based on the social metadata and fuse the web context into the multi-task learning for the interaction texts.
(3) A trust prediction from social psychology in signed social networks with friend and foe relationships is introduced here to handle the basic problem in influence diffusion procedure. Combining the basic conclusions from social psychology, the method is motivated by the matrix factorization in social recommendation. In the influence modeling, three factors are considered, the social preference of ourselves, the social preferences of our neighbors and finally the social preferences of the neighbors of our neighbors. A satisfactory result is reached in the trust prediction by matrix factorization considering the three kinds of social preferences.
(4) An influence maximization problem in human-intervened social networks and two basic approximated solutions are proposed. The traditional influence maximization problems are only considered the static social networks. Given the static social network, the influence diffuses with respect to the given budget and time following some influence diffusion models. Actually, the social networks are dynamically changed. Many social networks are operated according to the business interests by the service provider, which is what we considered in our work. As for the human-intervened social networks, some basic approximated algorithms are proposed for the influence maximization problem.
|游强. 基于交互关系的网络社区影响力分析方法[D]. 北京. 中国科学院大学,2016.|
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