# Homophily and missing links in citation networks

arXiv: https://arxiv.org/abs/1511.07643 · HTML: https://mlsanity.com/paper/1511.07643/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2015.

## Built on by (most cited first, 21 of 21)

- [Is Homophily a Necessity for Graph Neural Networks?](https://mlsanity.com/paper/2106.06134/) (2021)
- [Finding Global Homophily in Graph Neural Networks When Meeting Heterophily](https://mlsanity.com/paper/2205.07308/) (2022)
- [Graph Neural Networks for Graphs with Heterophily: A Survey](https://mlsanity.com/paper/2202.07082/) (2022)
- [Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?](https://mlsanity.com/paper/2309.16595/) (2023)
- [Graph Soft-Contrastive Learning via Neighborhood Ranking](https://mlsanity.com/paper/2209.13964/) (2022)
- [On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks](https://mlsanity.com/paper/2306.05557/) (2023)
- [RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning](https://mlsanity.com/paper/2204.13846/) (2022)
- [Localized Contrastive Learning on Graphs](https://mlsanity.com/paper/2212.04604/) (2022)
- [What Should I Cite? A RAG Benchmark for Academic Citation Prediction](https://mlsanity.com/paper/2601.14949/) (2026)
- [GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs](https://mlsanity.com/paper/2502.10522/) (2025)
- [OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization](https://mlsanity.com/paper/2302.00109/) (2023)
- [SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis](https://mlsanity.com/paper/2410.09510/) (2024)
- [Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion](https://mlsanity.com/paper/2510.10849/) (2025)
- [Detecting and analyzing missing citations to published scientific entities](https://mlsanity.com/paper/2210.10073/) (2022)
- [Generalized Laplacian Regularized Framelet Graph Neural Networks](https://mlsanity.com/paper/2210.15092/) (2022)
- [Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces](https://mlsanity.com/paper/2211.00565/) (2022)
- [Automated Polynomial Filter Learning for Graph Neural Networks](https://mlsanity.com/paper/2307.07956/) (2023)
- [Heterophily-informed Message Passing](https://mlsanity.com/paper/2504.19785/) (2025)
- [From scratch to silver: Creating trustworthy training data for patent-SDG classification using Large Language Models](https://mlsanity.com/paper/2509.09303/) (2025)
- [Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network](https://mlsanity.com/paper/2604.02342/) (2026)
- [Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning](https://mlsanity.com/paper/2604.08980/) (2026)

## Abstract (arXiv)

Citation networks have been widely used to study the evolution of science through the lenses of the underlying patterns of knowledge flows among academic papers, authors, research sub-fields, and scientific journals. Here we focus on citation networks to cast light on the salience of homophily, namely the principle that similarity breeds connection, for knowledge transfer between papers. To this end, we assess the degree to which citations tend to occur between papers that are concerned with seemingly related topics or research problems. Drawing on a large data set of articles published in the journals of the American Physical Society between 1893 and 2009, we propose a novel method for measuring the similarity between articles through the statistical validation of the overlap between their bibliographies. Results suggest that the probability of a citation made by one article to another is indeed an increasing function of the similarity between the two articles. Our study also enables us to uncover missing citations between pairs of highly related articles, and may thus help identify barriers to effective knowledge flows. By quantifying the proportion of missing citations, we conduct a comparative assessment of distinct journals and research sub-fields in terms of their ability to facilitate or impede the dissemination of knowledge. Findings indicate that knowledge transfer seems to be more effectively facilitated by journals of wide visibility, such as Physical Review Letters, than by lower-impact ones. Our study has important implications for authors, editors and reviewers of scientific journals, as well as public preprint repositories, as it provides a procedure for recommending relevant yet missing references and properly integrating bibliographies of papers.