# The Gene of Scientific Success

arXiv: https://arxiv.org/abs/2202.08461 · HTML: https://mlsanity.com/paper/2202.08461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining](https://mlsanity.com/paper/2008.07097/) (2020)
- [Predicting the long-term citation impact of recent publications](https://mlsanity.com/paper/1503.09156/) (2015)
- [Do altmetrics correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective](https://mlsanity.com/paper/1401.4321/) (2014)
- [From Observational Studies to Causal Rule Mining](https://mlsanity.com/paper/1508.03819/) (2015)
- [Can Scientific Impact Be Predicted?](https://mlsanity.com/paper/1606.05905/) (2016)
- [The Child is Father of the Man: Foresee the Success at the Early Stage](https://mlsanity.com/paper/1504.00948/) (2015)
- [Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models](https://mlsanity.com/paper/1512.08799/) (2015)
- [Choosing Collaboration Partners. How Scientific Success in Physics Depends on Network Positions](https://mlsanity.com/paper/1608.03251/) (2016)
- [The Role of Positive and Negative Citations in Scientific Evaluation](https://mlsanity.com/paper/2008.03844/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep Graph Learning for Anomalous Citation Detection](https://mlsanity.com/paper/2202.11360/) (2022)

## Abstract (arXiv)

This paper elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, and discovering potential cooperators etc. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard working. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.