Xiangfeng Luo

3.8k total citations
246 papers, 2.5k citations indexed

About

Xiangfeng Luo is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Xiangfeng Luo has authored 246 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 186 papers in Artificial Intelligence, 59 papers in Information Systems and 42 papers in Computer Vision and Pattern Recognition. Recurrent topics in Xiangfeng Luo's work include Cognitive Computing and Networks (57 papers), Topic Modeling (49 papers) and Advanced Text Analysis Techniques (48 papers). Xiangfeng Luo is often cited by papers focused on Cognitive Computing and Networks (57 papers), Topic Modeling (49 papers) and Advanced Text Analysis Techniques (48 papers). Xiangfeng Luo collaborates with scholars based in China, Australia and Hong Kong. Xiangfeng Luo's co-authors include Zheng Xu, Xiao Wei, Junyu Xuan, Lin Mei, Chuanping Hu, Yunhuai Liu, Hang Yu, Jie Lü, Guangquan Zhang and Jie Yu and has published in prestigious journals such as Nature Communications, European Journal of Operational Research and Expert Systems with Applications.

In The Last Decade

Xiangfeng Luo

222 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Xiangfeng Luo China	25	1.5k	589	511	303	289	246	2.5k
Dunja Mladenić Slovenia	29	1.7k 1.1×	879 1.5×	469 0.9×	222 0.7×	180 0.6×	168	2.9k
Tinghuai Ma China	28	1.4k 0.9×	575 1.0×	534 1.0×	672 2.2×	334 1.2×	196	2.8k
Yu Xie China	24	1.8k 1.2×	383 0.7×	379 0.7×	365 1.2×	228 0.8×	88	2.5k
Ziyu Guan China	28	1.6k 1.1×	923 1.6×	1.1k 2.1×	242 0.8×	336 1.2×	133	2.9k
Marko Grobelnik Slovenia	26	1.6k 1.0×	861 1.5×	362 0.7×	252 0.8×	160 0.6×	126	2.4k
Fei Hao China	26	853 0.6×	534 0.9×	258 0.5×	464 1.5×	318 1.1×	167	2.2k
Prem Melville United States	21	1.7k 1.2×	1.0k 1.7×	531 1.0×	224 0.7×	187 0.6×	40	2.7k
Kuansan Wang United States	23	2.0k 1.3×	960 1.6×	502 1.0×	207 0.7×	479 1.7×	65	3.1k
Cuiping Li China	23	1.2k 0.8×	684 1.2×	291 0.6×	358 1.2×	220 0.8×	120	2.1k
Jin Huang China	21	1.2k 0.8×	660 1.1×	720 1.4×	180 0.6×	168 0.6×	90	2.3k

Countries citing papers authored by Xiangfeng Luo

Since Specialization

Citations

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

Fields of papers citing papers by Xiangfeng Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiangfeng Luo

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

Wang, Xinzhi, et al.. (2025). FL-Evo: Jointly modeling fact and logic evolution patterns for temporal knowledge graph reasoning. Expert Systems with Applications. 286. 128081–128081.

Yu, Hang, Zhengyang Liu, & Xiangfeng Luo. (2024). Barely Supervised Learning for Graph-Based Fraud Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16548–16557. 12 indexed citations

Xie, Shaorong, Yang Li, Xinzhi Wang, et al.. (2024). Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative–competitive environments. Information Fusion. 108. 102318–102318. 7 indexed citations

Zhang, Zhenyu, Shaorong Xie, Han Zhang, Xiangfeng Luo, & Hang Yu. (2024). Zero-shot sim-to-real transfer using Siamese-Q-Based reinforcement learning. Information Fusion. 114. 102664–102664.

Zhang, Han, Hang Yu, Xiaoming Wang, et al.. (2024). Knowledge-guided communication preference learning model for multi-agent cooperation. Information Sciences. 667. 120395–120395. 3 indexed citations

Chen, Yuanzhu, et al.. (2024). Cognitive-based knowledge learning framework for recommendation. Knowledge-Based Systems. 287. 111446–111446. 4 indexed citations

Luo, Xiangfeng, et al.. (2024). Patent transformation prediction: When a patent can be transformed. Information Processing & Management. 61(6). 103872–103872. 2 indexed citations

Ma, Liyan, et al.. (2024). DP-DDCL: A discriminative prototype with dual decoupled contrast learning method for few-shot object detection. Knowledge-Based Systems. 297. 111964–111964. 4 indexed citations

Wang, Xinzhi, et al.. (2024). Hierarchical visual semantic guidance for enhanced relationship recognition in domain knowledge graphs. Engineering Applications of Artificial Intelligence. 137. 109040–109040.

10.

Xia, Nan, et al.. (2024). Knowledge Graph Reasoning via Dynamic Subgraph Attention with Low Resource Computation. Neurocomputing. 595. 127866–127866. 5 indexed citations

11.

Luo, Xiangfeng, et al.. (2024). An Adaptive Meta-Reinforcement Learning Algorithm for Simulation to Reality in Dynamic Scene. IEEE Transactions on Intelligent Vehicles. 10(2). 1168–1183.

12.

Wang, Xinzhi, et al.. (2024). Enhanced Implicit Sentiment Understanding With Prototype Learning and Demonstration for Aspect-Based Sentiment Analysis. IEEE Transactions on Computational Social Systems. 11(5). 5631–5646. 3 indexed citations

13.

Yu, Hang, et al.. (2023). THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection. Engineering Applications of Artificial Intelligence. 123. 106271–106271. 27 indexed citations

14.

Luo, Xiangfeng, et al.. (2023). Few-Shot Object Detection via Instance-wise and Prototypical Contrastive Learning. Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering. 2023. 685–690. 1 indexed citations

15.

Luo, Xiangfeng, et al.. (2019). Entity emotion mining in social media environment. Concurrency and Computation Practice and Experience. 31(20). 1 indexed citations

16.

Luo, Xiangfeng, et al.. (2018). Topic detection model in a single‐domain corpus inspired by the human memory cognitive process. Concurrency and Computation Practice and Experience. 30(19). 5 indexed citations

17.

Xu, Richard Yi Da, et al.. (2018). Semantic Emotion-Topic Model Based Social Emotion Mining.. Journal of Web Engineering. 17. 73–92. 2 indexed citations

18.

Liu, Weidong, et al.. (2017). Association Link Network Based Semantic Coherence Measurement for Short Texts of Web Events.. Journal of Web Engineering. 16. 39–62. 3 indexed citations

19.

Wang, Xingzhi, et al.. (2016). Outbreak Power Measurement for Evolution Course of Web Events.. Journal of Web Engineering. 15. 226–248. 1 indexed citations

20.

Gong, Zhiguo, et al.. (2011). Web Information Systems and Mining: International Conference, WISM 2011, Taiyuan, China, September 24-25, 2011, Proceedings, Part I. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 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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