Information Extraction to Improve Link Prediction in Scientific Social Networks

摘要

Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.