Xinjun Wang

463 citations
46 papers · 328 · h-index 9

Impact in

Papers in

    • Recommender Systems and Techniques 11
    • Web Data Mining and Analysis 5
    • Cloud Computing and Resource Management 4
    • Advanced Graph Neural Networks 8
    • Topic Modeling 6
    • Machine Learning in Healthcare 5

Xinjun Wang

39 papers receiving 326 citations

Peers

Xinjun Wang
Comparison fields: 5 of 80
  • Organic Chemistry 122
  • Information Systems 94
  • Surfaces, Coatings and Films 29
  • Biomaterials 42
  • Artificial Intelligence 95
Replace Zhiming Huang with:
Zhiming Huang China
Mario Boley Germany
Wenchuan Yang China
Jiahui Wen China
Kailong Chen China
Hisao Koizumi Japan
Qiannan Zhu China
Chunxiao Ye China
Xinjun Wang relative to Zhiming Huang China Zhiming Huang's profile →
Citations per field
00.5×3.9×
Zhiming Huang · 1×
Citations per year

Countries citing papers authored by Xinjun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xinjun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Xinjun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Xinjun Wang Line = papers co-authored together Xinjun Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015136
2 202221
3 202118
4 201316
5 201314
6 202213
7 201112
8 201810
9 20189
10 20237
11 20096
12 20215
13 20204
14 20214
15 20094
16 20124
17 20214
18 20163
19 20233
20 20223

About Xinjun Wang

Xinjun Wang is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Signal Processing and Applied Mathematics, having authored 46 papers that have together received 328 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Advanced Graph Neural Networks (8 papers), Data Management and Algorithms (6 papers), Topic Modeling (6 papers), Machine Learning in Healthcare (5 papers), Web Data Mining and Analysis (5 papers), Differential Equations and Boundary Problems (4 papers) and Cloud Computing and Resource Management (4 papers). The work is most often cited by research in Organic Chemistry (122 citations), Information Systems (94 citations), Surfaces, Coatings and Films (29 citations), Biomaterials (42 citations) and Artificial Intelligence (95 citations). Xinjun Wang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Baohua Zhang, Anqi Zhu, Zesheng An, Kai Ma, Xiao Wang, Yue Lv, Yuliang Shi, Zhongmin Yan, Wei Guo and Han Yu. Their work appears in journals such as Knowledge and Information Systems, IEEE Access, ACM Transactions on Knowledge Discovery from Data, Journal of Network and Computer Applications and Macromolecules.

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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