主题关注网络的表示学习

针对异质网络表示学习仅从结构方面考虑社交关系而忽略语义这一问题，结合用户间的社交关系和用户对主题的偏好两个方面，提出基于主题关注网络的表示学习算法。首先，针对主题关注网络的特点，结合集对分析理论的同异反（确定与不确定）思想，给出转移概率模型；然后，在转移概率模型的基础上提出了一种基于两类节点的随机游走算法，以得到相对高质量的随机游走序列；最后，基于序列中两类节点建模得到主题关注网络的嵌入向量空间表示。理论分析和在豆瓣数据集上的实验结果表明，结合转移概率模型的随机游走算法能更全面地分析网络中节点的连接关系，当划分社区的个数为13时，所提算法的模块度为0.699 8，相比metapath2vec算法提高了近5%，可以更详细地捕获网络中的信息。

Representation learning for topic-attention network

Concerning the problem that heterogeneous network representation learning only considers social relations in structure and ignores semantics， combining the social relationship between users and the preference of users for topics， a representation learning algorithm based on topic-attention network was proposed. Firstly， according to the characteristics of the topic-attention network and combining with the idea of the identical-discrepancy-contrary （determination and uncertainty） of set pair analysis theory， the transition probability model was given. Then， a random walk algorithm based on two types of nodes was proposed by using the transition probability model， so as to obtain the relatively high-quality random walk sequence. Finally， the embedding vector space representation of the topic-attention network was obtained by modeling based on two types of nodes in the sequences. Theoretical analysis and experimental results on the Douban dataset show that the random walk algorithm combined with the transition probability model is more comprehensive in analyzing the connection relationship between nodes in the network. The modularity of the proposed algorithm is 0.699 8 when the number of the communities is 13， which is nearly 5% higher than that of metapath2vec algorithm， and can capture more detailed information in the network.