Research On Models And Algorithms About Trust Computing And Mining Analysis Of Online Social Network

With the rapid development of information technology and widespread use of Internet, applications such as email,online shopping,online social networking,online payment and instant messaging have become an indispensable part of people's work and life.People ple are connected to each other through a variety of mutual relationships,forming many large,complicated and content-rich online social networks.For practical applications in business,current research of online social networks are facing two major challenges.First, due to the openness and anonymity of the lnternet,how to provide reasonable trust computing mechanism for users in online social networks is an urgent problem to solve.Second, online social network serving as a new business channel,how to conduct mining analysis on online social networks to achieve better economic and social benefits for practical applications also becomes a major concern.These two aspects are directly related to the safety and the utility of online social networks,therefore research about them both have theoretical value and practical significance.In the context of rapid development of online social networks,this dissertation conducts in-depth research on the above-mentioned two aspects and proposes some corresponding models and algorithms.The content and main contributions of this dissertation are as follows:*Reputation-Chain Trust Model for Online Social NetworksSince users can propagate trust information in social networks,this dissertation proposes a two-dimensional reputation-chain trust model which integrates trust values and reliable values.According to different application scenarios and users' personalized requirements,some trust calculation strategies are presented.Due to the openness and anonymity of the Internet,this dissertation introduces several mechanisms of reputation-chain trust model such as local storage mechanism,dynamic update mechanism and trust reporting mechanism.The experiment on the real social network of Epinions shows the flexibility and efficiency of reputation-chain trust model.*Semantic-based Trust ReasoningThis dissertation proposes an innovative semantic-based trust reasoning mechanism. This mechanism exploits the Semantic Web technology to define Epinions domain ontology,then makes use of OWL/RDF language for knowledge representation of Epinions data,and extracts trust-related information from the data.Based on these information,this dissertation defines OWL-based trust reasoning rules.Using these rules,we manage to reason about users' interested categories,and infer from overgeneralized trust relationships to category-specific trust relationships,and discover implicit trust relationships between users according to their feedback behavior,thus supporting more accurate and efficient trust calculation.*A Reputation-,Content- and Context-based Combined Trust ModelMost existing trust models are based on reputation information,however,only utilizing reputation information for trust computing is far from sufficient.Therefore,this dissertation innovatively puts forward a combined trust model-RCCtrust which integrates reputation,content and context information.RCCtrust utilizes semanticbased trust reasoning mechanism to extract trust-related information from the content and context of the Internet,and integrates users' similarity in product ratings and feedback behavior to depict the trust degree between pairs of users,thus constructing a edge-weighted combined trust network for trust computing.The experimental results show that RCCtrust model outperforms pure similarity method of traditional collaborative filtering and trust-aware method only utilizing trust relationships both in accuracy and coverage.*Heuristic Information-based Targeted Group DiscoveryThis dissertation proposes a targeted group discovery algorithm based on heuristic information in social networks.According to the features obtained from the analysis of Epinions online social network,combining with users roles and online behavior, we can extract heuristic information for targeted group discovery,thus effectively simplify the algorithm and reduce search space.The experimental results show that the heuristic information-based targeted group discover algorithm manages to efficiently locate influential targeted groups in the online social network and has a widespread application value in social network-based marketing.* Influence Maximization-based Key Persons MiningTraditional methods of mining key persons only considered the structural characteristics of social networks,but neglected the interactions between nodes.Concerning the above disadvantage,this dissertation proposes an influence maximization-based key persons mining algorithm.For the trust network on Epinions,this dissertation utilizes the extracted information from the content and context of Internet,models the influence relationships between users,and identifies key persons in the online social network by solving the influence maximization problem.The experimental results show that our proposed influence maximization-based hill-climbing algorithm outperforms the other methods in different ranges of activation threshold.The advantage of hill-climbing algorithm becomes more prominent especially when the activation threshold is large.