A Query-oriented Approach for Relevance in Citation Networks

Finding a relevant set of publications for a given topic of interest is a challenging problem. We propose a two-stage query-dependent approach for retrieving relevant papers given a keyword-based query. In the first stage, we utilize content similarity to select an initial seed set of publications; we then augment them by citation links weighted with information such as citation context relevance and age-based attenuation. In the second stage, we construct a multi-layer graph that expands the publications subgraph by including links to the authors, venues, and keywords. This allows us to return recommendations that are both highly authoritative, and also textually related to the query. We show that our staged approach gives superior results on three different benchmark query sets.