Metapath-Guided Credit Allocation for Identifying Representative Works

The ability to identify the representative works of a researcher has important implications in a wide range of areas, including hiring, funding, and promotion systems. In this paper, we propose a metapath-guided credit allocation method (MGCA) for identifying the representative works of individual researchers. MGCA utilizes metapath-guided neighbours to exploit rich semantic information in heterogeneous information network for locating a researcher’s field of expertise, and explicitly captures the importance of a paper, its relevance to other papers, and the unequally distributed contribution of each citation via a two-step credit allocation. We validate MGCA by applying it on the American Physical Society dataset in the scenario of identifying the Nobel prize winning papers of the Nobel laureates. Experiments demonstrate that the proposed method can significantly outperform the existing approaches.