Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks
This work addresses the challenge of leveraging multiple relationships in complex social networks for tasks like identifying spammers, but it is incremental as it extends an existing method with supervision.
The paper tackles the problem of node classification in multi-relational social networks by proposing a semi-supervised tensor factorization method that incorporates class labels into the RESCAL framework, resulting in more accurate models as shown in evaluations on real-world data.
This paper proposes a method to guide tensor factorization, using class labels. Furthermore, it shows the advantages of using the proposed method in identifying nodes that play a special role in multi-relational networks, e.g. spammers. Most complex systems involve multiple types of relationships and interactions among entities. Combining information from different relationships may be crucial for various prediction tasks. Instead of creating distinct prediction models for each type of relationship, in this paper we present a tensor factorization approach based on RESCAL, which collectively exploits all existing relations. We extend RESCAL to produce a semi-supervised factorization method that combines a classification error term with the standard factor optimization process. The coupled optimization approach, models the tensorial data assimilating observed information from all the relations, while also taking into account classification performance. Our evaluation on real-world social network data shows that incorporating supervision, when available, leads to models that are more accurate.