# Graduate Employment Prediction with Bias

arXiv: https://arxiv.org/abs/1912.12012 · HTML: https://mlsanity.com/paper/1912.12012/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Graph Lifelong Learning: A Survey](https://mlsanity.com/paper/2202.10688/) (2022)
- [Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks](https://mlsanity.com/paper/2202.07832/) (2022)
- [FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning](https://mlsanity.com/paper/2407.19429/) (2024)
- [Surf or sleep? Understanding the influence of bedtime patterns on campus](https://mlsanity.com/paper/2202.09283/) (2022)
- [Graph Federated Learning Based on the Decentralized Framework](https://mlsanity.com/paper/2307.09801/) (2023)
- [MEGA: Second-Order Gradient Alignment for Catastrophic Forgetting Mitigation in GFSCIL](https://mlsanity.com/paper/2504.13691/) (2025)

## Abstract (arXiv)

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.