社交网络用户时态属性及时态关系模型研究

Social network (SN) continuously produces vast amounts of valuable user data, so it has received a lot of attention in academia and industry. Meanwhile, SN user analysis has become an important research subject. Time is a ubiquitous objective property of the nature. With the deepening and development of research on SN user analysis and its applications, the demand of analysis of SN user attribute and relation with temporal semantics is also growing. This project takes SN user as the research object and research the temporal attribute and temporal relation models of SN user based on time dimension. The specific works will include: (1) Research methods of semantic abstraction and calculus of user temporal attribute and temporal label extraction; (2) Research methods of user temporal relation calculus and temporal correlation strength computation; (3) Research the application technologies of SN user temporal attribute and temporal relation models. This project will solve some key science problems on temporal semantic of SN user and provide a novel approach for SN user analysis. Furthermore, it will develop and enrich theories and methods of temporal data processing.

社交网络（Social Network）不断产生海量有价值的用户数据，因此受到学术界和工业界的大量关注，社交网络用户分析已成为研究热点。时间是自然界无处不在的客观属性，随着社交网络用户分析研究及应用工作的不断深入和发展，结合时态语义对用户属性及关系信息进行分析的需求也在不断增长。本项目以社交网络用户为研究对象，引入时间维度研究社交网络用户的时态属性和时态关系模型，主要工作包括：（1）研究社交网络用户时态属性语义抽象、演算体系及时态标签抽取方法；（2）研究社交网络用户时态关系演算及时态关联强度计算方法；（3）研究基于社交网络用户时态属性及时态关系模型的应用模式。本项目预期拟解决社交网络用户分析时态语义相关的关键科学问题，为社交网络用户分析提供新途径和新方法，同时丰富和扩展时态数据处理的理论和方法。

时间是自然界无处不在的客观属性，随着社交网络用户分析研究及应用工作的不断深入和发展，结合时态语义对用户属性及关系信息进行分析的需求也在不断增长。本项目以社交网络用户为研究对象，引入时间维度研究社交网络用户的时态属性和时态关系模型，主要研究成果包括：（1）社交网络用户时态属性抽取与用户关系强度计算方法；（2）社交网络移动对象与时态数据索引技术；（3）社交网络用户时态社区发现模型及基于学术社交网络的时态信息管理技术；（4）在学者网（www.scholat.com）发布社区发现与链路预测等4个公共数据集和时态学者知识图谱。项目研究成果为社交网络时态信息管理和分析提供了新途径和新方法，同时丰富和扩展时态数据处理的理论和方法。

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