Interdisciplinarity revealed by transitive reduction of citation networks
Abstract
Abstract We investigate the impact of transitive reduction on citation networks. Our hypothesis is that documents which lose fewer citations under transitive reduction are likely to be multi-class (our definition of interdisciplinary), while a large loss of citations suggests a document is primarily cited within a single class. We test this hypothesis by using an artificial model of a citation network and by using data on citations from three sources: academic papers, court decisions and patents. Where needed, we applied modularity-based clustering techniques on a network defined using the undirected version of the citation network to classify documents by communities. A cluster-dependent measure was then used to classify the nodes as multi-class or single-class. Our results provide strong support for our hypothesis in all four cases.
Fields of Study & Subjects
Complex Network Analysis Techniques
Bioinformatics and Genomic Networks
Advanced Graph Neural Networks
Publication details
- Journal / source
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Publication date
- 22 September 2026
- Language
- EN