Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks

TL;DR

This paper introduces a semi-supervised tensor factorization method based on RESCAL for node classification in multi-relational social networks, improving accuracy by integrating class labels into the factorization process.

Contribution

It extends RESCAL tensor factorization to incorporate supervision, enabling better identification of key nodes like spammers in complex networks.

Findings

Supervised tensor factorization improves node classification accuracy.

The method effectively identifies influential nodes such as spammers.

Incorporating class labels enhances the interpretability of the model.

Abstract

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.