Link and interaction polarity predictions in signed networks.

In today’s world, users typically take to online social media to express their opinions, which can inherently be both positive and negative. In fact, online social networks can be best modeled as signed networks, where opinions in the form of positive and negative links can exist between users, such as our friends and foes (e.g., “unfriended” users), respectively. Furthermore, users can also express their opinions to content generated by others through online social interactions, such as commenting or rating. Intuitively, these two types of opinions in the form of links and interactions should be related. For example, users’ interactions are likely to be positive (or negative) to those they have positively (or negatively) established links with. Similarly, we tend to establish positive (or negative) links with those whose generated content we frequently positively (or negatively) interact with online. Hence, in this paper, we first verify these assumptions by understanding the correlation between these two types of opinions from both a local and global perspective. Then, we propose a framework that jointly solves the link and interaction polarity prediction problem based on our newly found understanding of how these two problems are correlated. We ultimately perform experiments on a real-world signed network to demonstrate the effectiveness of our proposed approach to help mitigate both the data sparsity and cold-start problems found in the two tasks of link and interaction polarity prediction.