Many real-world relations can be represented by signed networks with positive and negative
links, as a result of which signed network analysis has attracted increasing attention from …

Many complex systems in the real world can be modeled as signed social networks that
contain both positive and negative relations. Algorithms for mining social networks have …

Different from a large body of research on social networks that has focused almost
exclusively on positive relationships, we study signed social networks with both positive and …

Signed network analysis has attracted increasing attention in recent years. This is in part
because research on signed network analysis suggests that negative links have added …

Node classification in social networks has been proven to be useful in many real-world
applications. The vast majority of existing algorithms focus on unsigned social networks (or …

Link prediction in signed social networks is an important and challenging problem in social
network analysis. To produce the most accurate prediction results, two questions must be …

Signed directed networks with positive or negative links convey rich information such as like
or dislike, trust or distrust. Existing work of sign prediction mainly focuses on triangles (triadic …

We consider the problem of link prediction in signed networks. Such networks arise on the
web in a variety of ways when users can implicitly or explicitly tag their relationship with …

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 …

Trust networks, where people leave trust and distrust feedback, are becoming increasingly
common. These networks may be regarded as signed graphs, where a positive edge weight …