中文摘要：

随着社交网络服务的快速发展及增长,理解网络用户之间潜在的影响力的传播过程,能够帮助用户更好地理解网络结构的动态演化,以及不同的信息对于人与人之间社会关系的影响作用.现有的影响力传播相关的研究工作主要集中在给定静态社交网络结构,分析用户之间的影响力传播,找出最具有影响力的用户子集.然而大部分已有工作都忽略了社交网络中的内容信息,即用户之间的影响力作用是与用户产生内容紧密相关的.该文提出了一种融合内容信息和社交网络动态时间特性的潜在影响力传播模型InfoIBP（Influence propagation on Indian Buffet Process）.网络中有影响力的用户被看作是一种潜在的特征,可通过不同采样算法和数值逼近求解出来.而对于网络动态时间特性,借助于隐马尔可夫模型来建模不同时间步上的影响力传播过程.在数据集DBLP和Digg上的一系列链接预测、偏好预测和运行时间评测等实验,证明了所提InfoIBP模型能够更准确地建模潜在的影响力传播过程,更有效地挖掘出社交网络中的有影响力用户及更全面地描述网络的动态时间特性,并能对未来的观测数据做出相对精准的预测.

英文摘要：

With the proliferation of diversified social network services,understanding how the influence is propagated could help us apprehend the network evolution mechanism and the social impact of different kinds of information better.Most previous works have focused on the analysis of the influence propagation on the static network structure and the discovery of the subset of the most influential users.They fail to identify the user susceptibility delivered by user generated content.In this paper,we propose the InfoIBP（Influence propagation on Indian Buffet Process）model,ageneral framework for the latent influence propagation on social content with dynamic network structure,which based on the Indian buffet process.The influential users could be taken as the latent features in the social network and be found by different sampling algorithms based on numerical approximation.For the dynamic evolutional property of the network,hidden Markov model was adopted to describe the influence propagation in different time steps.A series ofexperiments for link prediction,preference prediction and running time evaluation are conducted on the DBLP and Digg datasets.The results show that the InfoIBP is more accurate and more efficient for modeling the latent influence propagation and discovering the influential users.It also can describe the dynamic evolutional property more comprehensively and achieve relatively accurate predictions for the future observations.