Wei Ju

1.7k total citations · 1 hit paper
75 papers, 1.0k citations indexed

About

Wei Ju is a scholar working on Artificial Intelligence, Surgery and Information Systems. According to data from OpenAlex, Wei Ju has authored 75 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 23 papers in Surgery and 10 papers in Information Systems. Recurrent topics in Wei Ju's work include Advanced Graph Neural Networks (28 papers), Organ Transplantation Techniques and Outcomes (20 papers) and Recommender Systems and Techniques (10 papers). Wei Ju is often cited by papers focused on Advanced Graph Neural Networks (28 papers), Organ Transplantation Techniques and Outcomes (20 papers) and Recommender Systems and Techniques (10 papers). Wei Ju collaborates with scholars based in China, United States and Canada. Wei Ju's co-authors include Ming Zhang, Xiao Luo, Yifang Qin, Yiyang Gu, Xian‐Sheng Hua, Chong Chen, Yifan Wang, Minghua Deng, Yusheng Zhao and Meng Qu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Wei Ju

68 papers receiving 994 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.

A Comprehensive Survey on Deep Graph Representation Learning

2024 138 citations Wei Ju, Zheng Fang et al. Neural Networks profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wei Ju China	21	597	207	207	120	89	75	1.0k
Xuejun Zhang China	17	386 0.6×	128 0.6×	82 0.4×	29 0.2×	48 0.5×	118	1.1k
Yifan Wang China	11	189 0.3×	86 0.4×	95 0.5×	71 0.6×	26 0.3×	52	601
Caiming Zhong China	13	369 0.6×	75 0.4×	271 1.3×	121 1.0×	34 0.4×	42	632
Yifang Qin China	14	357 0.6×	169 0.8×	129 0.6×	67 0.6×	58 0.7×	37	644
Chun-Nam Yu United States	10	818 1.4×	70 0.3×	698 3.4×	13 0.1×	82 0.9×	16	1.5k
Hwanjo Yu South Korea	16	731 1.2×	158 0.8×	169 0.8×	172 1.4×	138 1.6×	42	1.1k
D. Dumitrescu Romania	13	260 0.4×	82 0.4×	44 0.2×	21 0.2×	40 0.4×	99	732
Yu Xue China	13	454 0.8×	119 0.6×	178 0.9×	96 0.8×	198 2.2×	22	898

Countries citing papers authored by Wei Ju

Since Specialization

Citations

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

Fields of papers citing papers by Wei Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Ju

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

Luo, Junyu, Xiao Luo, Xiusi Chen, et al.. (2025). Semi-supervised Fine-tuning for Large Language Models. 2795–2808.

2.

Zhao, Yusheng, et al.. (2025). Embracing Large Language Models in Traffic Flow Forecasting. 8108–8123. 1 indexed citations

3.

Ren, Tao, et al.. (2025). MHGC: Multi-scale hard sample mining for contrastive deep graph clustering. Information Processing & Management. 62(4). 104084–104084. 6 indexed citations

4.

Wang, Yifan, Yiyang Gu, Zhiping Xiao, et al.. (2025). Hypergraph Consistency Learning With Relational Distillation. IEEE Transactions on Multimedia. 27. 7028–7039.

5.

Qin, Yifang, Wei Ju, Yiyang Gu, et al.. (2025). PolyCF: Towards Optimal Spectral Graph Filters for Collaborative Filtering. ACM Transactions on Information Systems. 43(4). 1–28. 2 indexed citations

6.

Ju, Wei, Yusheng Zhao, Yifang Qin, et al.. (2024). COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting. Information Fusion. 107. 102341–102341. 30 indexed citations

7.

Wang, Yifan, Xiao Luo, Chong Chen, et al.. (2024). DisenSemi: Semi-Supervised Graph Classification via Disentangled Representation Learning. IEEE Transactions on Neural Networks and Learning Systems. 36(5). 8192–8204. 16 indexed citations

8.

Ju, Wei, Zheng Fang, Yiyang Gu, et al.. (2024). A Comprehensive Survey on Deep Graph Representation Learning. Neural Networks. 173. 106207–106207. 138 indexed citations breakdown →

9.

Long, Qingqing, et al.. (2024). MOAT: Graph Prompting for 3D Molecular Graphs. 1586–1596. 1 indexed citations

10.

Qin, Yifang, et al.. (2024). Learning Graph ODE for Continuous-Time Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering. 36(7). 3224–3236. 34 indexed citations

11.

Ju, Wei, Yifan Wang, Zhiping Xiao, et al.. (2024). Learning Knowledge-diverse Experts for Long-tailed Graph Classification. ACM Transactions on Knowledge Discovery from Data. 19(2). 1–24. 1 indexed citations

12.

Luo, Xiao, Yusheng Zhao, Yifang Qin, Wei Ju, & Ming Zhang. (2023). Towards Semi-Supervised Universal Graph Classification. IEEE Transactions on Knowledge and Data Engineering. 36(1). 416–428. 30 indexed citations

13.

Ju, Wei, et al.. (2023). RAHNet: Retrieval Augmented Hybrid Network for Long-tailed Graph Classification. 3817–3826. 10 indexed citations

14.

Qin, Yifang, et al.. (2023). A Diffusion Model for POI Recommendation. ACM Transactions on Information Systems. 42(2). 1–27. 35 indexed citations

15.

Luo, Xiao, Wei Ju, Yiyang Gu, et al.. (2023). Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning. ACM Transactions on Knowledge Discovery from Data. 18(2). 1–23. 45 indexed citations

16.

Ju, Wei, et al.. (2023). Few-shot Molecular Property Prediction via Hierarchically Structured Learning on Relation Graphs. Neural Networks. 163. 122–131. 44 indexed citations

17.

Ju, Wei, Yifang Qin, Xiao Luo, et al.. (2023). Redundancy-Free Self-Supervised Relational Learning for Graph Clustering. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 18313–18327. 22 indexed citations

18.

Qin, Yifang, Yifan Wang, Fang Sun, et al.. (2023). DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation. 508–516. 46 indexed citations

19.

Luo, Xiao, Wei Ju, Meng Qu, et al.. (2022). CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning. IEEE Transactions on Neural Networks and Learning Systems. 35(1). 899–912. 52 indexed citations

20.

Ju, Wei, et al.. (2022). GHNN: Graph Harmonic Neural Networks for semi-supervised graph-level classification. Neural Networks. 151. 70–79. 37 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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