# Transitive Reduction of Citation Networks

arXiv: https://arxiv.org/abs/1310.8224 · HTML: https://mlsanity.com/paper/1310.8224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2013.

## Built on by (most cited first, 4 of 4)

- [Lifted Inference beyond First-Order Logic](https://mlsanity.com/paper/2308.11738/) (2023)
- [Weighted First Order Model Counting with Directed Acyclic Graph Axioms](https://mlsanity.com/paper/2302.09830/) (2023)
- [How to measure the optimality of word or gesture order with respect to the principle of swap distance minimization](https://mlsanity.com/paper/2604.01938/) (2026)
- [From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles](https://mlsanity.com/paper/2609.07673/) (2026)

## Abstract (arXiv)

In many complex networks the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal structure. We illustrate our approach using citation networks formed from academic papers, patents, and US Supreme Court verdicts. We show how transitive reduction reveals fundamental differences in the citation practices of different areas, how it highlights particularly interesting work, and how it can correct for the effect that the age of a document has on its citation count. Finally, we transitively reduce null models of citation networks with similar degree distributions and show the difference in degree distributions after transitive reduction to illustrate the lack of causal structure in such models.