Abstract:
Representation learning in online social networks has been an important research task for better service, which targets at learning the low-dimensional vector representation for nodes in a network. There exists a kind of social network, which not only includes the topological structure and node attributes, but other information, such as the user behaviors. It is necessary to use these behaviors to learn the node representations. In this paper, we propose FB2vec to analyze forwarding behaviors and achieve better node representations. Moreover, an information intensity function based on the utility function is proposed to measure the possibility of forwarding behaviors. However, the intensity function can not reflect exact possibility value and only provide a relative intensity order. Therefore, we sample the intensity order pairs from datasets and train the intensity function to adapt original orders by an attribute-reserved siamese network. Extensive experiments demonstrate the effectiveness of FB2vec and the visualization of information intensity function indicates the rationality of FB2vec.
