Network embedding is to learn low-dimensional vector representations for nodes of a given social network, facilitating many tasks in social network analysis such as link prediction. The vast majority of existing embedding algorithms are designed for unsigned social networks or social networks with only positive links. However, networks in social media could have both positive and negative links, and little work exists for signed social networks. From recent findings of signed network analysis, it is evident that negative links have distinct properties and added value besides positive links, which brings about both challenges and opportunities for signed network embedding. In this paper, we propose a deep learning framework SiNE for signed network embedding. The framework optimizes an objective function guided by social theories that provide a fundamental understanding of signed social networks. Experimental results on two realworld datasets of social media demonstrate the effectiveness of the proposed framework SiNE.
Wang et al. REF introduce a new objective function for signed network embedding guided by extended structural balance theory and propose a deep learning framework SiNE.
Signed Network Embedding in Social Media
SDM
Computer Science