# Forecasting Faculty Placement from Patterns in Co-authorship Networks

arXiv: https://arxiv.org/abs/2507.14696 · HTML: https://mlsanity.com/paper/2507.14696/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 88 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://mlsanity.com/paper/1612.07659/) (2016)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs](https://mlsanity.com/paper/2107.01495/) (2021)
- [Historical comparison of gender inequality in scientific careers across countries and disciplines](https://mlsanity.com/paper/1907.04103/) (2019)
- [Predicting Scientific Success Based on Coauthorship Networks](https://mlsanity.com/paper/1402.7268/) (2014)
- [Quantifying the impact of weak, strong, and super ties in scientific careers](https://mlsanity.com/paper/1509.01804/) (2015)
- [Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks](https://mlsanity.com/paper/1602.00795/) (2016)
- [Achieving competitive advantage in academia through early career coauthorship with top scientists](https://mlsanity.com/paper/1906.04619/) (2019)
- [Gender and collaboration patterns in a temporal scientific authorship network](https://mlsanity.com/paper/2005.13512/) (2020)
- [The Chaperone Effect in Scientific Publishing](https://mlsanity.com/paper/1812.10181/) (2018)
- [Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact](https://mlsanity.com/paper/2003.12519/) (2020)
- [Ranking mobility and impact inequality in early academic careers](https://mlsanity.com/paper/2303.15988/) (2023)
- [The misleading narrative of the canonical faculty productivity trajectory](https://mlsanity.com/paper/1612.08228/) (2016)
- [Prestige drives epistemic inequality in the diffusion of scientific ideas](https://mlsanity.com/paper/1805.09966/) (2018)
- [The contribution of star scientists to overall sex differences in research productivity](https://mlsanity.com/paper/1811.08696/) (2018)
- [Can Author Collaboration Reveal Impact? The Case of h-index](https://mlsanity.com/paper/2104.05562/) (2021)
- [Predicting publication productivity for authors: Shallow or deep architecture?](https://mlsanity.com/paper/2104.14114/) (2021)
- [Labor advantages drive the greater productivity of faculty at elite universities](https://mlsanity.com/paper/2204.05989/) (2022)
- [The structure of segregation in co-authorship networks and its impact on scientific production](https://mlsanity.com/paper/2207.09800/) (2022)
- [Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network](https://mlsanity.com/paper/2506.04435/) (2025)

## Abstract (arXiv)

Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies have found strong correlations between faculty hiring and attributes such as doctoral department prestige and publication record, they rarely assess whether these associations generalize to individual hiring outcomes, particularly for future candidates outside the original sample. Here, we consider faculty placement as an individual-level prediction task. Our data consist of temporal co-authorship networks with conventional attributes such as doctoral department prestige and bibliometric features. We observe that using the co-authorship network significantly improves predictive accuracy by up to 10% over traditional indicators alone, with the largest gains observed for placements at the most elite (top-10) departments. Our results underscore the role that social networks, professional endorsements, and implicit advocacy play in faculty hiring beyond traditional measures of scholarly productivity and institutional prestige. By introducing a predictive framing of faculty placement and establishing the benefit of considering co-authorship networks, this work provides a new lens for understanding structural biases in academia that could inform targeted interventions aimed at increasing transparency, fairness, and equity in academic hiring practices.