Measure the Impact of Institution and Paper via Institution-citation Network

TL;DR

This paper introduces IPRank, a novel model that measures the impact of institutions and papers simultaneously within a heterogeneous citation network, effectively identifying influential research and prestigious universities.

Contribution

The paper presents IPRank, a new impact measurement model utilizing heterogeneous structural measures and PageRank to evaluate both institutions and papers concurrently.

Findings

IPRank outperforms existing methods in identifying impactful institutions.

The model effectively highlights universities with Nobel laureates.

Experimental results validate the model's ability to reflect research impact.

Abstract

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.