🤖 AI Summary
This paper addresses the limitations of text-based approaches for interdisciplinary literature identification—namely, high computational cost and poor interpretability—by proposing a purely network-structural method. It models citation networks as directed acyclic graphs (DAGs) and introduces “diversity centrality,” a novel metric that integrates transitive reduction with degree centrality to identify pivotal papers bridging densely connected, multi-disciplinary subgroups. By applying topological reduction to eliminate redundant transitive paths, the method accentuates cross-domain hub papers. Experiments across multiple real-world citation networks demonstrate that the approach achieves interdisciplinary impact detection performance comparable to state-of-the-art text-analytic methods, while being computationally efficient, parameter-free, and fully interpretable. It thus establishes a scalable, transparent, and structurally grounded paradigm for assessing interdisciplinary research.
📝 Abstract
We study transitivity in directed acyclic graphs and its usefulness in capturing nodes that act as bridges between more densely interconnected parts in such type of network. In transitively reduced citation networks degree centrality could be used as a measure of interdisciplinarity or diversity. We study the measure's ability to capture "diverse" nodes in random directed acyclic graphs and citation networks. We show that transitively reduced degree centrality is capable of capturing "diverse" nodes, thus this measure could be a timely alternative to text analysis techniques for retrieving papers, influential in a variety of research fields.
