# Predicting Scientific Success Based on Coauthorship Networks

arXiv: https://arxiv.org/abs/1402.7268 · HTML: https://mlsanity.com/paper/1402.7268/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2014.

## Built on by (most cited first, 8 of 8)

- [A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles](https://mlsanity.com/paper/2104.04939/) (2021)
- [Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnology](https://mlsanity.com/paper/2106.02110/) (2021)
- [Minorities in networks and algorithms](https://mlsanity.com/paper/2206.07113/) (2022)
- [Supervised Hypergraph Reconstruction](https://mlsanity.com/paper/2211.13343/) (2022)
- [Proximity Matters: Analyzing the Role of Geographical Proximity in Shaping AI Research Collaborations](https://mlsanity.com/paper/2406.06662/) (2024)
- [MARIOH: Multiplicity-Aware Hypergraph Reconstruction](https://mlsanity.com/paper/2504.00522/) (2025)
- [Forecasting Faculty Placement from Patterns in Co-authorship Networks](https://mlsanity.com/paper/2507.14696/) (2025)
- [Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball](https://mlsanity.com/paper/2609.05574/) (2026)

## Abstract (arXiv)

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.