大规模社会网络信息处理优化技术研究

大规模社会网络是大数据的一种，具有数据关联度高，非结构化等特点。本申请拟对大规模社会网络中的信息处理进行优化研究。主要包括两个方面：第一：优化竞争性社会网络中的信息传播模型，并研究适应大规模竞争性网络环境的可扩展性谣言抑制和特定信息传播最大化方法。第二：建模和规范社会网络中的新经济行为。研究社会网络中用户结合个人评价和朋友推荐购买商品的方法，发现商品价格和整体利润的关系。结合博弈论和大数据分析，设计并优化适合大规模社会网络的经济行为公平保障机制。

Large-scale social networks belong to “big data”, they are highly correlated and unstructured data. This application is intended for the optimization techniques for information retrieval in large-scale social networks. Two aspects will be studied. The first is to model information propagation in competitive social networks more accurately, and design scalable algorithms for rumor restriction and influence maximization in large-scale competitive social networks where multiple information existed. The second is the modeling and regulation of new economic behavior appeared in social networks. Check how social network users combine their own personal evaluations and recommendations from their friends to purchase products, and find the relationship between product price and overall profit. We plan to adopt game theory and big data analysis, to design optimized fairness guarantee mechanism of economic behavior in large-scale social networks.