Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery

摘要

In signed social networks, users are connected via directional signed links that indicate their opinions about each other. Predicting the signs of such links is crucial for many real-world applications, such as recommendation systems. The authors mine patterns that emerge frequently in the social graph, and show that such patterns possess enough discriminative power to accurately predict the relationships among social network users. They evaluate their approach through an experimental study that comprises three large-scale, real-world datasets and show that it outperforms state-of-the art methods.