Semantic Social Network Analysis by Cross-Domain Tensor Factorization

Abstract

Analyzing 'what topics' a user discusses with others is important in social network analysis. Since social relationships can be represented as multiobject relationships (e.g., those composed of a user, another user, and the topic of communication), they can be naturally represented as a tensor. By factorizing the tensor, we can perform communication prediction that predicts links among users and the topics discussed among them. The prediction accuracy, however, is often inadequate for applications because: 1) users usually discuss a variety of topics, and thus the prediction results tend to be biased toward popular domains and 2) topics that are rarely discussed among users trigger the sparsity problem in tensor factorization. Our solution, cross-domain tensor factorization (CrTF), first determines the topic domain by analyzing communication logs among users using the DBpedia knowledge base and creates a tensor composed of users, other users, and the topics of communication for each domain; it avoids strong bias toward particular domains. It then simultaneously factorizes tensors across domains while integrating semantics from DBpedia into factorizations; this solves the sparsity problem. Experiments using Twitter data sets show that CrTF achieves higher accuracy than the state-of-the-art tensor-based methods and extracts key topics and social influencers for each domain.