Detecting topic-level influencers in large-scale scientific networks

Scientific networks play an increasingly important role in facilitating knowledge and technique diffusion. In such networks, highly influential nodes (scientists or literatures) are prone to stimulate other researchers in the generation of innovative ideas. The objective of this study is to detect topic-level influencers from a large collection of links between nodes and textual contents in scientific networks. For this purpose, we propose a sparse link topic model (SLTM) that introduces a “Spike and Slab” prior to achieve sparsity in node-topic distribution. Compared with previous approaches, our model assumes that a node usually focuses on several salient topics instead of a wide range of topics, which is useful in learning topic-level influencers in scientific networks. In addition, a collapsed variational Bayesian (CVB) inference algorithm is designed for large-scale applications. Our experiments are conducted on a large scientific collaboration network. The results reveal that the proposed model significantly improves the precision of topic-level detection. Our analysis also reflects that SLTM can explicitly model the sparse topical structure of each node in the network.