Finding seminal scientific publications with graph mining

Abstract: We investigate the applicability of network analysis to the problem of finding seminal publications in scientific publishing. In particular, we focus on the network measures betweenness centrality, the so-called backbone graph, and the burstiness of citations. The metrics are evaluated using precision-related scores with respect to gold standards based on fellow programmes and manual annotation. Citation counts, PageRank, and random selection are used as baselines. We find that the backbone graph provides us with a way to possibly discover seminal publications with low citation count, and combining betweenness and burstiness gives results on par with citation count.