Haiping Yu

24 total papers · 574 total citations
14 papers, 483 citations indexed

About

Haiping Yu is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Artificial Intelligence. According to data from OpenAlex, Haiping Yu has authored 14 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 7 papers in Information Systems and 4 papers in Artificial Intelligence. Recurrent topics in Haiping Yu's work include Recommender Systems and Techniques (7 papers), Advanced Graph Neural Networks (3 papers) and Image Retrieval and Classification Techniques (2 papers). Haiping Yu is often cited by papers focused on Recommender Systems and Techniques (7 papers), Advanced Graph Neural Networks (3 papers) and Image Retrieval and Classification Techniques (2 papers). Haiping Yu collaborates with scholars based in China and France. Haiping Yu's co-authors include Yiteng Pan, Fazhi He, Fazhi He, Xiao Chen, Kang Li, Kang Li, Xiaohong Chen, Haoran Li, Geng Chen and Yuquan Zhu and has published in prestigious journals such as IEEE Access, Neurocomputing and Neural Computing and Applications.

In The Last Decade

Haiping Yu

12 papers receiving 467 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Haiping Yu	221	194	178	61	26	14	483
Roobaea Alroobaea	144 0.7×	129 0.7×	91 0.5×	94 1.5×	28 1.1×	39	509
Peng Dai	235 1.1×	202 1.0×	150 0.8×	28 0.5×	12 0.5×	18	436
Ajeet Ram Pathak	220 1.0×	190 1.0×	71 0.4×	31 0.5×	30 1.2×	19	541
Liwei Wu	176 0.8×	316 1.6×	145 0.8×	30 0.5×	36 1.4×	28	524
Yi Lu	90 0.4×	166 0.9×	55 0.3×	49 0.8×	17 0.7×	14	501
Xiao Yu	123 0.6×	210 1.1×	81 0.5×	100 1.6×	32 1.2×	34	545
Jian Wan	220 1.0×	77 0.4×	92 0.5×	105 1.7×	12 0.5×	39	479
Feng Wang	242 1.1×	245 1.3×	53 0.3×	16 0.3×	17 0.7×	15	504
Jie Zhou	239 1.1×	224 1.2×	48 0.3×	31 0.5×	11 0.4×	27	500
Wenwen Gong	143 0.6×	200 1.0×	182 1.0×	174 2.9×	6 0.2×	21	539

Countries citing papers authored by Haiping Yu

Since Specialization

Citations

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

Fields of papers citing papers by Haiping Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haiping Yu

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

All Works

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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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