Research Of Trust Network Extraction And Subgraph Matching Based On Social Attribute Constraints

With the development of Internet information technology and the popularity of social software,there egerge a large quantity of objects with social characteristic information on the Internet.Social relations among those objects are complex,constituting a large social network.In real life,various applications based on social network,such as the selection of service providers,the crowdsourcing service,special group mining and so on,make people's lives more colorful.So the relevant technology of the social network is becoming more and more important.This thesis focuses on trust network exaction and subgraph matching on the basis of social attribute constraints.It aims to solve the social network problems in different application scenarios by proposing a novel model structure and efficient algorithm.The specific contents are as follows:(1)As to the problem of trust network extraction in social networks,a model of trust network extraction based on heuristic social situational awareness is proposed.By extending the k-best-first Search(KBFS)method,several optimization strategies and methods put forward to make the model better than the existing methods in terms of efficiency and the extracted trust network quality.(2)An algorithm based on M-HAMC is proposed to solve the problem of subgraph matching based on social attribute constraints.This algorithm adopts a two-way and multi-thread search method,which avoids traversing all the combinations.And it combines with the effective SSC graph compression and index,which improves the query processing efficiency.(3)To implement all methods and compare them with the existing methods in terms of efficiency and effectiveness by conducting experiments.