Wei Hu

6.2k total citations · 1 hit paper
109 papers, 2.4k citations indexed

About

Wei Hu is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Wei Hu has authored 109 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Artificial Intelligence, 34 papers in Management Science and Operations Research and 26 papers in Information Systems. Recurrent topics in Wei Hu's work include Topic Modeling (32 papers), Data Quality and Management (32 papers) and Advanced Graph Neural Networks (31 papers). Wei Hu is often cited by papers focused on Topic Modeling (32 papers), Data Quality and Management (32 papers) and Advanced Graph Neural Networks (31 papers). Wei Hu collaborates with scholars based in China, United States and Australia. Wei Hu's co-authors include Yuzhong Qu, Zequn Sun, Qinghe Zhang, Gong Cheng, Muhao Chen, Chengming Wang, Lingbing Guo, Jian S. Dai, Farahnaz Akrami and Chengkai Li and has published in prestigious journals such as Advanced Materials, Angewandte Chemie International Edition and SHILAP Revista de lepidopterología.

In The Last Decade

Wei Hu

98 papers receiving 2.3k 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.

Bootstrapping Entity Alignment with Knowledge Graph Embedding

2018 303 citations Zequn Sun, Wei Hu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wei Hu China	24	2.0k	855	600	337	192	109	2.4k
Junliang Yu China	19	1.6k 0.8×	249 0.3×	1.7k 2.8×	37 0.1×	407 2.1×	49	2.3k
Jian-Tao Sun China	24	1.8k 0.9×	102 0.1×	1.0k 1.7×	82 0.2×	409 2.1×	56	2.6k
Jialei Wang United States	18	789 0.4×	210 0.2×	152 0.3×	96 0.3×	251 1.3×	35	1.3k
Daojian Zeng China	13	2.7k 1.4×	292 0.3×	427 0.7×	319 0.9×	305 1.6×	39	3.0k
Omid Madani United States	16	960 0.5×	176 0.2×	633 1.1×	40 0.1×	260 1.4×	43	1.5k
Peng Jiang China	18	1.2k 0.6×	472 0.6×	1.5k 2.6×	22 0.1×	476 2.5×	58	2.0k
Jiaheng Lu China	23	892 0.4×	312 0.4×	725 1.2×	28 0.1×	248 1.3×	96	2.0k
Jianwen Zhang China	12	2.5k 1.3×	496 0.6×	515 0.9×	227 0.7×	388 2.0×	36	2.8k
Yuzhong Qu China	23	2.2k 1.1×	777 0.9×	849 1.4×	358 1.1×	155 0.8×	106	2.6k
Ron Bekkerman United States	15	862 0.4×	183 0.2×	467 0.8×	68 0.2×	216 1.1×	35	1.3k

Countries citing papers authored by Wei Hu

Since Specialization

Citations

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

Fields of papers citing papers by Wei Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wei Hu

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

Sun, Zequn, et al.. (2025). Transfer-and-Fusion: Integrated Link Prediction Across Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering. 37(5). 3062–3074.

2.

Sun, Zequn, et al.. (2024). Generating Explanations to Understand and Repair Embedding-Based Entity Alignment. 2205–2217. 4 indexed citations

3.

Hu, Wei, et al.. (2024). Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion. 7957–7977. 2 indexed citations

4.

Hu, Wei, Bolong Huang, Mingzi Sun, et al.. (2024). Doping Ti into RuO₂ to Accelerate Bridged‐Oxygen‐Assisted Deprotonation for Acidic Oxygen Evolution Reaction. Advanced Materials. 37(3). e2411709–e2411709. 43 indexed citations

5.

Liu, Yuwen, Tao Zhang, Jie Chen, et al.. (2024). Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections. Frontiers in Pediatrics. 12. 1398713–1398713. 1 indexed citations

6.

Wang, Yuxin, et al.. (2023). Open-world story generation with structured knowledge enhancement: A comprehensive survey. Neurocomputing. 559. 126792–126792. 7 indexed citations

7.

Sun, Zequn, et al.. (2023). Deep Active Alignment of Knowledge Graph Entities and Schemata. Proceedings of the ACM on Management of Data. 1(2). 1–26. 7 indexed citations

8.

Wang, Yuxin, et al.. (2023). Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4217–4224. 14 indexed citations

9.

Wang, Xinyi, et al.. (2023). Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction. 12693–12706. 6 indexed citations

10.

Sun, Zequn, Muhao Chen, & Wei Hu. (2021). Knowing the No-match: Entity Alignment with Dangling Cases. 3582–3593. 14 indexed citations

11.

Sun, Zequn, Muhao Chen, Wei Hu, et al.. (2020). Knowledge Association with Hyperbolic Knowledge Graph Embeddings. 5704–5716. 46 indexed citations

12.

Wang, Difeng, et al.. (2020). Global-to-Local Neural Networks for Document-Level Relation Extraction. 3711–3721. 78 indexed citations

13.

Hu, Wei, et al.. (2020). Provable Benefits of Representation Learning in Linear Bandits.. arXiv (Cornell University).

14.

Kuditipudi, Rohith, Xiang Wang, Yi Zhang, et al.. (2019). Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. arXiv (Cornell University). 32. 14574–14583. 5 indexed citations

15.

Hu, Wei, et al.. (2019). Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering. 2614–2622. 28 indexed citations

16.

Zaveri, Amrapali, Wei Hu, & Michel Dumontier. (2019). MetaCrowd: Crowdsourcing Biomedical Metadata Quality Assessment. 6. 98–112. 3 indexed citations

17.

Arora, Sanjeev, Wei Hu, & Pravesh K. Kothari. (2018). An Analysis of the t-SNE Algorithm for Data Visualization. Conference on Learning Theory. 1455–1462. 5 indexed citations

18.

Hu, Wei, et al.. (2017). njuLink: results for instance matching at OAEI 2017.. 158–165. 1 indexed citations

19.

Hu, Wei, Yuanyuan Zhao, Dan Li, et al.. (2007). Falcon-AO: results for OAEI 2007. Brain Imaging and Behavior. 17(6). 170–178. 11 indexed citations

20.

Stuckenschmidt, Heiner, Jérôme Euzenat, Andreas Heß, et al.. (2006). D2.2.4: Alignment implementation and benchmarking results. Biological Chemistry. 379(10). 1279–86. 3 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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