Yuanfu Lu

1.0k total citations
12 papers, 637 citations indexed

About

Yuanfu Lu is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics. According to data from OpenAlex, Yuanfu Lu has authored 12 papers receiving a total of 637 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Information Systems and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Yuanfu Lu's work include Advanced Graph Neural Networks (10 papers), Complex Network Analysis Techniques (5 papers) and Recommender Systems and Techniques (5 papers). Yuanfu Lu is often cited by papers focused on Advanced Graph Neural Networks (10 papers), Complex Network Analysis Techniques (5 papers) and Recommender Systems and Techniques (5 papers). Yuanfu Lu collaborates with scholars based in China, Singapore and United States. Yuanfu Lu's co-authors include Chuan Shi, Yuan Fang, Linmei Hu, Zhiyuan Liu, Xiao Wang, Yanfang Ye, Philip S. Yu, Ruijia Wang, Xiao Wang and Peng Cui and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, World Wide Web and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Yuanfu Lu

12 papers receiving 627 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuanfu Lu China	10	538	284	207	100	62	12	637
Shaohua Fan China	8	571 1.1×	247 0.9×	178 0.9×	138 1.4×	99 1.6×	14	714
Aravind Sankar United States	7	416 0.8×	162 0.6×	185 0.9×	88 0.9×	78 1.3×	13	536
Brandon Norick United States	10	861 1.6×	637 2.2×	302 1.5×	106 1.1×	174 2.8×	12	1.0k
Jianxin Ma China	10	448 0.8×	331 1.2×	97 0.5×	127 1.3×	86 1.4×	15	599
Fei Cai China	16	543 1.0×	463 1.6×	67 0.3×	116 1.2×	59 1.0×	55	738
Yantao Jia China	14	508 0.9×	169 0.6×	69 0.3×	61 0.6×	50 0.8×	40	649
Huawei Shen China	14	337 0.6×	149 0.5×	174 0.8×	63 0.6×	44 0.7×	44	526
Qiaoyu Tan United States	12	359 0.7×	222 0.8×	52 0.3×	104 1.0×	57 0.9×	27	478
Baoxu Shi United States	11	499 0.9×	138 0.5×	80 0.4×	72 0.7×	39 0.6×	21	651
Woojeong Jin United States	10	488 0.9×	163 0.6×	88 0.4×	126 1.3×	42 0.7×	16	565

Countries citing papers authored by Yuanfu Lu

Since Specialization

Citations

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

Fields of papers citing papers by Yuanfu Lu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuanfu Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Yuanfu Lu. A scholar is included among the top collaborators of Yuanfu Lu 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 Yuanfu Lu. Yuanfu Lu 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:

12 of 12 papers shown

1.

Wang, Wei, et al.. (2024). Group-to-group recommendation with neural graph matching. World Wide Web. 27(2). 2 indexed citations

2.

Yang, Cheng, et al.. (2022). Few-shot Link Prediction in Dynamic Networks. 1245–1255. 33 indexed citations

3.

Xie, Ruobing, Yalong Wang, Rui Wang, et al.. (2022). Long Short-Term Temporal Meta-learning in Online Recommendation. 1168–1176. 18 indexed citations

4.

Lu, Yuanfu, et al.. (2021). Learning to Pre-train Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 4276–4284. 70 indexed citations

5.

Wang, Wei, et al.. (2021). Influence Maximization in Multi-Relational Social Networks. 4193–4202. 9 indexed citations

6.

Li, Chen, Yuanfu Lu, Wei Wang, et al.. (2021). Package Recommendation with Intra- and Inter-Package Attention Networks. 595–604. 10 indexed citations

7.

Lu, Yuanfu, et al.. (2021). Contrastive Pre-Training of GNNs on Heterogeneous Graphs. 803–812. 33 indexed citations

8.

Wang, Xiao, et al.. (2020). Dynamic Heterogeneous Information Network Embedding With Meta-Path Based Proximity. IEEE Transactions on Knowledge and Data Engineering. 34(3). 1117–1132. 94 indexed citations

9.

Shi, Chuan, Yuanfu Lu, Linmei Hu, Zhiyuan Liu, & Huadóng Ma. (2020). RHINE: Relation Structure-Aware Heterogeneous Information Network Embedding. IEEE Transactions on Knowledge and Data Engineering. 34(1). 433–447. 26 indexed citations

10.

Lu, Yuanfu, Yuan Fang, & Chuan Shi. (2020). Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. 1563–1573. 166 indexed citations

11.

Lu, Yuanfu, Xiao Wang, Chuan Shi, Philip S. Yu, & Yanfang Ye. (2019). Temporal Network Embedding with Micro- and Macro-dynamics. 469–478. 91 indexed citations

12.

Lu, Yuanfu, Chuan Shi, Linmei Hu, & Zhiyuan Liu. (2019). Relation Structure-Aware Heterogeneous Information Network Embedding. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 4456–4463. 85 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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