Community Splitter: A Network Embedding Method for Predicting Missing Links

Shuo Wang, Ziqiang Wu, Sheng Bi, Zheng Zhang, Xiaomin Huang, Ming-Yang Zhou, Hao Liao

Abstract: Networks are one of the most powerful structures for modeling problems in the real world. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding learns from feature representations of nodes and links in a network, and converts it to vectors. Community structure is an important feature of the network, which represents the relationship among nodes and attracts the attention of relevant researchers. Many algorithms have been developed to identify the community structure. These algorithms usually identify different communities in the network, generating different types of information. In this paper, we propose a "Community Splitter" model based on random walk and RNN (Recurrent Neural Networks) that combines the node information generated by multiple community detection algorithms to improve node representation and link prediction. Extensive experiments on nine real datasets demonstrate that our proposed Community Splitter model has a significant prediction power compared to state-of-the-art link prediction models.