Wei Jin

3.3k total citations · 3 hit papers
56 papers, 1.7k citations indexed

About

Wei Jin is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Wei Jin has authored 56 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 11 papers in Molecular Biology and 7 papers in Information Systems. Recurrent topics in Wei Jin's work include Advanced Graph Neural Networks (22 papers), Topic Modeling (12 papers) and Adversarial Robustness in Machine Learning (7 papers). Wei Jin is often cited by papers focused on Advanced Graph Neural Networks (22 papers), Topic Modeling (12 papers) and Adversarial Robustness in Machine Learning (7 papers). Wei Jin collaborates with scholars based in United States, China and Hong Kong. Wei Jin's co-authors include Jiliang Tang, Yao Ma, Xiaorui Liu, Suhang Wang, Yiqi Wang, Xianfeng Tang, Xiaoyang Wang, Jian Yu, Caiyan Jia and Xin Wang and has published in prestigious journals such as IEEE Transactions on Image Processing, Genome biology and Engineering Applications of Artificial Intelligence.

In The Last Decade

Wei Jin

48 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

2020 383 citations Yao Ma, Wei Jin et al. profile →
Graph Structure Learning for Robust Graph Neural Networks

2020 362 citations Wei Jin, Yao Ma et al. profile →
Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

2024 73 citations Wei Jin, Hongzhi Wen et al. profile →

Peers

Wei Jin

AI

BC

IS

TRANS

CVPR

Ting Zhong China

Shaojie Qiao China

Kun He China

Toyotaro Suzumura Japan

Xiang Fei China

Li Kuang China

Qimai Li Hong Kong

Xiaoming Zhang China

Lei Zhao China

Tomoharu Iwata Japan

Ting Zhong China

Wei Jin

817 ×0.8

AI

304 ×1.1

BC

334 ×1.3

IS

467 ×2.0

TRANS

214 ×0.9

CVPR

Citations per year, relative to Wei Jin Wei Jin (= 1×) peers Ting Zhong

Countries citing papers authored by Wei Jin

Since Specialization

Citations

This map shows the geographic impact of Wei Jin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wei Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Jin more than expected).

Fields of papers citing papers by Wei Jin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wei Jin. The network helps show where Wei Jin may publish in the future.

Co-authorship network of co-authors of Wei Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Wei Jin. A scholar is included among the top collaborators of Wei Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wei Jin. Wei Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Yang, Carl, et al.. (2025). Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks. PubMed. 2025. 6118–6128.

2.

Yang, Kyu Hwan, et al.. (2024). Spectral-Aware Augmentation for Enhanced Graph Representation Learning. 2837–2847. 1 indexed citations

3.

Jin, Wei, Haohan Wang, Daochen Zha, et al.. (2024). DCAI: Data-centric Artificial Intelligence. 1482–1485. 2 indexed citations

4.

Wen, Hongzhi, et al.. (2024). Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective. 932–943. 3 indexed citations

5.

Chang, Yurui, et al.. (2024). Globally Interpretable Graph Learning via Distribution Matching. 992–1002. 2 indexed citations

6.

Prakash, B. Aditya, et al.. (2024). A Review of Graph Neural Networks in Epidemic Modeling. 6577–6587. 19 indexed citations

7.

Chen, Huaming, Jun Zhuang, Wei Jin, et al.. (2024). Trustworthy and Responsible AI for Information and Knowledge Management System. 5574–5576.

8.

Wen, Hongzhi, Yixin Wang, Wei Jin, et al.. (2024). Deep Learning in Single-cell Analysis. ACM Transactions on Intelligent Systems and Technology. 15(3). 1–62. 8 indexed citations

9.

Wen, Hongzhi, Wei Jin, Yixin Wang, et al.. (2024). DANCE: a deep learning library and benchmark platform for single-cell analysis. Genome biology. 25(1). 72–72. 8 indexed citations

10.

Jin, Wei, et al.. (2023). Toward Degree Bias in Embedding-Based Knowledge Graph Completion. arXiv (Cornell University). 705–715. 12 indexed citations

11.

Jin, Wei, et al.. (2023). Enhancing Graph Representations Learning with Decorrelated Propagation. 1466–1476. 4 indexed citations

12.

Jin, Wei, et al.. (2023). Learning Representations for Hyper-Relational Knowledge Graphs. 253–257. 7 indexed citations

13.

Liu, Xiaorui, Wei Jin, Han Xu, et al.. (2021). Graph Neural Networks with Adaptive Residual. Neural Information Processing Systems. 34. 16 indexed citations

14.

Jin, Wei, et al.. (2020). Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study. 25 indexed citations

15.

Jin, Wei, et al.. (2019). Data Fusion Approach for Evaluating Route Choice Models in Large-Scale Complex Urban Rail Transit Networks. Journal of Transportation Engineering Part A Systems. 146(1). 5 indexed citations

16.

Singh, Abhishek, Eduardo Blanco, & Wei Jin. (2019). Incorporating Emoji Descriptions Improves Tweet Classification. University of North Texas Digital Library (University of North Texas). 2096–2101. 40 indexed citations

17.

Jha, Kishlay, et al.. (2019). A survey on literature based discovery approaches in biomedical domain. Journal of Biomedical Informatics. 93. 103141–103141. 30 indexed citations

18.

Peng, Yan, et al.. (2013). INTELLIGENT TUTORS IN IMMERSIVE VIRTUAL ENVIRONMENTS. International Association for Development of the Information Society. 2 indexed citations

19.

Jin, Wei, et al.. (2003). The Design and Analysis of Increment Services over DVB-C Network. 1 indexed citations

20.

Jin, Wei, et al.. (1998). Issues in Developing Very Large Data Warehouses. Very Large Data Bases. 633–636. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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