Aiwei Liu

88 total papers · 438 total citations
28 papers, 176 citations indexed

About

Aiwei Liu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Aiwei Liu has authored 28 papers receiving a total of 176 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Aiwei Liu's work include Topic Modeling (15 papers), Natural Language Processing Techniques (14 papers) and Electronic Packaging and Soldering Technologies (4 papers). Aiwei Liu is often cited by papers focused on Topic Modeling (15 papers), Natural Language Processing Techniques (14 papers) and Electronic Packaging and Soldering Technologies (4 papers). Aiwei Liu collaborates with scholars based in China, United States and Hong Kong. Aiwei Liu's co-authors include Xuming Hu, Lijie Wen, Philip S. Yu, Guangwen Hu, Yufeng Wu, Fu Guo, Lin Li, Xuming Hu, Yawen Yang and Irwin King and has published in prestigious journals such as Journal of Materials Science, ACM Computing Surveys and Applied Surface Science.

In The Last Decade

Aiwei Liu

22 papers receiving 175 citations

Author Peers

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

Author						Papers	Cites
Aiwei Liu	116	31	27	27	22	28	176
R. Srinivasan	72 0.6×	25 0.8×	23 0.9×	17 0.6×	12 0.5×	29	206
Xi Chen	103 0.9×	48 1.5×	52 1.9×	17 0.6×	14 0.6×	35	247
Jingqing Jiang	84 0.7×	24 0.8×	34 1.3×	12 0.4×	18 0.8×	30	177
Prakhar Gupta	109 0.9×	24 0.8×	22 0.8×	6 0.2×	29 1.3×	27	199
Dhafar Hamed	115 1.0×	23 0.7×	39 1.4×	7 0.3×	10 0.5×	29	223
Rishabh Bhardwaj	61 0.5×	6 0.2×	13 0.5×	11 0.4×	88 4.0×	30	224
Md. Tabil Ahammed	76 0.7×	34 1.1×	10 0.4×	4 0.1×	55 2.5×	42	241
Danyang Liu	103 0.9×	43 1.4×	56 2.1×	16 0.6×	5 0.2×	21	188
Xue Chen	156 1.3×	24 0.8×	153 5.7×	15 0.6×	74 3.4×	26	291
Yuanfeng Song	119 1.0×	30 1.0×	38 1.4×	9 0.3×	26 1.2×	45	177

Countries citing papers authored by Aiwei Liu

Since Specialization

Citations

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

Fields of papers citing papers by Aiwei Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aiwei Liu

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