# Bi-SCORE for Weighted Bipartite Networks with Application in Knowledge Source Discovery

## TL;DR

This paper introduces Bi-SCORE, a spectral clustering method tailored for weighted bipartite networks, effectively uncovering community structures in citation networks to reveal key research areas in statistics.

## Contribution

The paper presents Bi-SCORE, a novel spectral clustering algorithm with theoretical guarantees, specifically designed for weighted bipartite networks with degree heterogeneity, applied to citation data.

## Key findings

- Bi-SCORE successfully detects six distinct research communities.
- The method outperforms existing community detection techniques.
- Application reveals meaningful insights into statistical knowledge flow.

## Abstract

Community detection in citation networks offers a powerful approach to understanding knowledge flow and identifying core research areas within academic disciplines. This study focuses on knowledge source discovery in statistics by analyzing a weighted bipartite journal citation network constructed from 16,119 articles published in eight core journals from 2001 to 2023. To capture the inherent asymmetry of citation behavior, we explicitly preserve the bipartite structure of the network, distinguishing between citing and cited journals. For this task, we propose Bi-SCORE (Bipartite Spectral Clustering on Ratios-of-Eigenvectors), a computationally efficient and initialization-free spectral method designed for community detection in weighted bipartite networks with degree heterogeneity. We establish rigorous theoretical guarantees for the performance of Bi-SCORE under the weighted bipartite degree-corrected stochastic block model. Furthermore, simulation studies demonstrate its robustness across varying levels of sparsity and degree heterogeneity, where it outperforms existing methods. When applied to the real-world citation network, Bi-SCORE uncovers a six-community structure corresponding to key research areas in statistics, including applied statistics, methodology, theory, computation, and econometrics. These findings provide valuable insights into the intricate citation patterns and knowledge flow among statistical journals.