Xutan Peng

498 total citations
13 papers, 95 citations indexed

About

Xutan Peng is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Xutan Peng has authored 13 papers receiving a total of 95 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 3 papers in Information Systems and 3 papers in Management Science and Operations Research. Recurrent topics in Xutan Peng's work include Topic Modeling (9 papers), Advanced Graph Neural Networks (8 papers) and Data Quality and Management (3 papers). Xutan Peng is often cited by papers focused on Topic Modeling (9 papers), Advanced Graph Neural Networks (8 papers) and Data Quality and Management (3 papers). Xutan Peng collaborates with scholars based in United Kingdom, China and United States. Xutan Peng's co-authors include Jianxin Li, Hao Peng, Chen Li, Lihong Wang, Mark Stevenson, Chenghua Lin, Shanghang Zhang, Guanyi Chen, Lifang He and Shu Guo and has published in prestigious journals such as Neurocomputing, Neural Networks and Knowledge-Based Systems.

In The Last Decade

Xutan Peng

12 papers receiving 94 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xutan Peng United Kingdom	7	91	20	10	8	7	13	95
Zhanlin Sun China	3	65 0.7×	8 0.4×	13 1.3×	5 0.6×	4 0.6×	4	75
Kiril Gashteovski Germany	6	162 1.8×	13 0.7×	8 0.8×	9 1.1×	11 1.6×	17	168
Guntis Bārzdiņš Latvia	5	77 0.8×	12 0.6×	12 1.2×	6 0.8×	11 1.6×	20	88
Mohamed Amir Yosef Germany	4	90 1.0×	13 0.7×	6 0.6×	4 0.5×	10 1.4×	5	98
Luke Vilnis United States	6	202 2.2×	22 1.1×	29 2.9×	10 1.3×	16 2.3×	13	208
Sujith Ravi United States	7	90 1.0×	9 0.5×	26 2.6×	5 0.6×	5 0.7×	10	101
Sylvain Lamprier France	6	62 0.7×	4 0.2×	15 1.5×	7 0.9×	3 0.4×	15	80
Stephen Mussmann United States	5	84 0.9×	5 0.3×	24 2.4×	3 0.4×	5 0.7×	10	93
Stefanos Angelidis United Kingdom	5	186 2.0×	7 0.3×	11 1.1×	4 0.5×	5 0.7×	6	200

Countries citing papers authored by Xutan Peng

Since Specialization

Citations

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

Fields of papers citing papers by Xutan Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xutan Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Xutan Peng. A scholar is included among the top collaborators of Xutan Peng 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 Xutan Peng. Xutan Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

1.

Li, Qian, Jianxin Li, Jia Wu, et al.. (2024). Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment. Neural Networks. 179. 106479–106479. 11 indexed citations

2.

Peng, Xutan, et al.. (2024). Selective Run-Length Encoding. 576–576.

3.

Li, Qian, Shu Guo, Cheng Ji, et al.. (2023). Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction. 8982–8994. 6 indexed citations

4.

Peng, Xutan, Yipeng Zhang, Jingfeng Yang, & Mark Stevenson. (2023). On the Vulnerabilities of Text-to-SQL Models. 1–12. 1 indexed citations

5.

Peng, Xutan, et al.. (2022). Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework That Works. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 5 indexed citations

6.

Peng, Xutan, Guanyi Chen, Chenghua Lin, & Mark Stevenson. (2021). Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis. arXiv (Cornell University). 11 indexed citations

7.

Li, Chen, Xutan Peng, Hao Peng, Jianxin Li, & Lihong Wang. (2021). TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification via Structure-Sensitive Interpolation. 2680–2686. 15 indexed citations

8.

Li, Chen, Xutan Peng, Shanghang Zhang, et al.. (2021). Learning graph attention-aware knowledge graph embedding. Neurocomputing. 461. 516–529. 17 indexed citations

9.

Li, Chen, Xutan Peng, Hao Peng, et al.. (2021). Graph-based Semi-Supervised Learning by Strengthening Local Label Consistency. 29. 3201–3205. 1 indexed citations

10.

Peng, Xutan, Chenghua Lin, & Mark Stevenson. (2021). Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation Norm Optimisation. arXiv (Cornell University). 6 indexed citations

11.

Li, Chen, Xutan Peng, Lifang He, et al.. (2021). Cross-knowledge-graph entity alignment via relation prediction. Knowledge-Based Systems. 240. 107813–107813. 16 indexed citations

12.

Li, Chen, Xutan Peng, Shanghang Zhang, et al.. (2020). Modeling relation paths for knowledge base completion via joint adversarial training. Knowledge-Based Systems. 201-202. 105865–105865. 4 indexed citations

13.

Li, Chen, Xutan Peng, Hao Peng, et al.. (2020). Forming an Electoral College for a Graph: a Heuristic Semi-supervised Learning Framework.. 2 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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