Chuan Shi

15.9k total citations · 7 hit papers
224 papers, 7.7k citations indexed

About

Chuan Shi is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics. According to data from OpenAlex, Chuan Shi has authored 224 papers receiving a total of 7.7k indexed citations (citations by other indexed papers that have themselves been cited), including 172 papers in Artificial Intelligence, 70 papers in Information Systems and 53 papers in Statistical and Nonlinear Physics. Recurrent topics in Chuan Shi's work include Advanced Graph Neural Networks (118 papers), Recommender Systems and Techniques (52 papers) and Complex Network Analysis Techniques (52 papers). Chuan Shi is often cited by papers focused on Advanced Graph Neural Networks (118 papers), Recommender Systems and Techniques (52 papers) and Complex Network Analysis Techniques (52 papers). Chuan Shi collaborates with scholars based in China, United States and Singapore. Chuan Shi's co-authors include Philip S. Yu, Xiao Wang, Binbin Hu, Bin Wu, Yuanfu Lu, Yitong Li, Yuan Fang, Jiawei Zhang, Yizhou Sun and Cheng Yang and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Chuan Shi

200 papers receiving 7.5k citations

Hit Papers

5 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.

A survey of heterogeneous information network analysis

2016 687 citations Chuan Shi et al. IEEE Transactions on Knowledge and Data Engineering profile →
Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model

2018 425 citations Chuan Shi et al. profile →
Beyond Low-frequency Information in Graph Convolutional Networks

2021 294 citations Deyu Bo, Xiao Wang et al. profile →
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

2019 243 citations Shaohua Fan, Chuan Shi et al. profile →
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning

2021 235 citations Xiao Wang, Hui Han et al. profile →
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

2022 225 citations Xiao Wang, Deyu Bo et al. IEEE Transactions on Big Data profile →
Heterogeneous Graph Structure Learning for Graph Neural Networks

2021 187 citations Xiao Wang, Chuan Shi et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chuan Shi China	46	5.8k	3.0k	2.0k	1.2k	904	224	7.7k
Yuxiao Dong China	29	3.7k 0.6×	1.6k 0.5×	1.5k 0.7×	756 0.6×	575 0.6×	115	5.7k
Peng Cui China	48	7.1k 1.2×	2.5k 0.8×	3.7k 1.9×	2.2k 1.9×	1.2k 1.3×	219	10.4k
Jundong Li United States	40	4.2k 0.7×	1.4k 0.5×	962 0.5×	1.1k 0.9×	778 0.9×	199	6.5k
Aditya Grover United States	11	4.5k 0.8×	1.5k 0.5×	2.3k 1.2×	826 0.7×	748 0.8×	29	7.5k
Yizhou Sun United States	44	7.2k 1.2×	3.6k 1.2×	3.5k 1.8×	1.3k 1.1×	1.6k 1.8×	206	10.5k
Cheng Yang China	28	3.5k 0.6×	1.2k 0.4×	1.2k 0.6×	1.0k 0.9×	681 0.8×	148	6.5k
Xueqi Cheng China	52	7.2k 1.2×	3.4k 1.1×	2.5k 1.3×	1.5k 1.3×	1.3k 1.4×	517	11.6k
Aixin Sun Singapore	45	4.9k 0.8×	3.7k 1.2×	1.1k 0.5×	1.3k 1.1×	836 0.9×	223	8.7k
Jing Jiang Singapore	39	5.1k 0.9×	2.4k 0.8×	1.3k 0.6×	954 0.8×	715 0.8×	169	7.5k
Hanghang Tong United States	47	5.7k 1.0×	2.1k 0.7×	3.3k 1.6×	2.6k 2.3×	1.9k 2.1×	327	10.5k

Countries citing papers authored by Chuan Shi

Since Specialization

Citations

This map shows the geographic impact of Chuan Shi'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 Chuan Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chuan Shi more than expected).

Fields of papers citing papers by Chuan Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chuan Shi. 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 Chuan Shi. The network helps show where Chuan Shi may publish in the future.

Co-authorship network of co-authors of Chuan Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Chuan Shi. A scholar is included among the top collaborators of Chuan Shi 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 Chuan Shi. Chuan Shi 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.

Hu, Michael Y., et al.. (2025). Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases. 9691–9709.

2.

Yang, Cheng, et al.. (2025). Data-centric Prompt Tuning for Dynamic Graphs. 2336–2345.

3.

Shi, Chuan, et al.. (2025). MILS: Modality Interaction Driven Learning for Logic Synthesis. 64–70.

4.

Wang, Xiao, et al.. (2025). Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?. 2008–2019. 2 indexed citations

5.

Shi, Chuan, et al.. (2025). PIRLLS: Pretraining with Imitation and RL Finetuning for Logic Synthesis. 65–71.

6.

Ma, Ang, et al.. (2024). Cross-view hypergraph contrastive learning for attribute-aware recommendation. Information Processing & Management. 61(4). 103701–103701. 11 indexed citations

7.

Yang, Cheng, et al.. (2024). Endowing Pre-trained Graph Models with Provable Fairness. arXiv (Cornell University). 1045–1056. 4 indexed citations

8.

Li, Yibo, et al.. (2024). Graph Fairness Learning under Distribution Shifts. 676–684. 1 indexed citations

9.

Bo, Deyu, et al.. (2024). Data-Centric Graph Learning: A Survey. IEEE Transactions on Big Data. 11(1). 1–20. 3 indexed citations

10.

Wu, Lingling, et al.. (2023). The Chinese Version of Oxford Depression Questionnaire: A Validation Study in Patients with Mood Disorders. Neuropsychiatric Disease and Treatment. Volume 19. 547–556. 2 indexed citations

11.

Shi, Chuan, et al.. (2023). Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation. Applied Sciences. 13(15). 8885–8885. 4 indexed citations

12.

Shi, Chuan, et al.. (2023). Distance Information Improves Heterogeneous Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering. 36(3). 1030–1043. 2 indexed citations

13.

Yang, Cheng, et al.. (2023). Learning to Distill Graph Neural Networks. 123–131. 7 indexed citations

14.

Yang, Cheng, et al.. (2023). GammaGL: A Multi-Backend Library for Graph Neural Networks. 2861–2870. 2 indexed citations

15.

Zhang, Ming, et al.. (2023). Stigmatized experience is associated with exacerbated pain perception in depressed patients. Behaviour Research and Therapy. 161. 104252–104252. 3 indexed citations

16.

Hu, Linmei, Tianchi Yang, Wanjun Zhong, et al.. (2021). Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge. 754–763. 120 indexed citations

17.

Hu, Linmei, Chen Li, Cheng Yang, et al.. (2020). Graph Neural News Recommendation with Unsupervised Preference Disentanglement. 4255–4264. 82 indexed citations

18.

Hu, Linmei, et al.. (2019). Improving Distantly-Supervised Relation Extraction with Joint Label Embedding. Monash University Research Portal (Monash University). 3819–3827. 30 indexed citations

19.

Shi, Chuan, et al.. (2018). A Meta Path Based Method for Entity Set Expansion in Knowledge Graph. IEEE Transactions on Big Data. 8(3). 616–629. 5 indexed citations

20.

Liu, Gang, et al.. (2016). Retweet Number Prediction Based on Retweet Propagation Process. 44(12). 2996. 1 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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