A decentralised approach for link inference in large signed graphs

Social networks as large graphs have interesting information embedded within. The presence of links between nodes characterises the underlying relationships between nodes. Link inference is an interesting problem and has been studied for unsigned and signed graphs. Whilst the signed links give more insight into the node relationships, the class imbalance and the limited availability of signed graph datasets obstructs the studies in this domain. Furthermore, the studies in literature usually consider a single large graph and ignore the underlying potentially different sub-graphs in the original graph. In this work, we consider signed graphs for link inference with a focus on negative links and adopt a decentralised approach to learn the graph and sub-graph embeddings, i.e., we consider sub-graphs of the original signed graph for link inference. As we focus on negative links, the problem becomes more challenging due to the class-imbalance and sparsity of the sub-graphs. For the input graph, we employ a decentralised approach to learn the latent factors in the sub-graphs using probabilistic matrix factorisation. We perform an extensive experimental study using real datasets to assess the applicability and effectiveness of the approach. The results show that the decentralised approach is a promising consideration and gives encouraging results for the performance and scalability of the solution.