Xinjun Wang

475 citations
46 papers · 332 · h-index 9

Impact in

Papers in

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

Xinjun Wang

39 papers receiving 330 citations

Peers

Xinjun Wang
Comparison fields: 5 of 77
  • Information Systems 98
  • Organic Chemistry 123
  • Surfaces, Coatings and Films 29
  • Biomaterials 42
  • Artificial Intelligence 97
Replace Mario Boley with:
Mario Boley Germany
Wenchuan Yang China
Rongfeng Zheng China
Jiahui Wen China
Kailong Chen China
Qiannan Zhu China
Hisao Koizumi Japan
Xinjun Wang relative to Mario Boley Germany Mario Boley's profile →
Citations per field
00.5×5.9×
Mario Boley · 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 2015137
2 202222
3 202119
4 201316
5 202214
6 201314
7 201112
8 201810
9 20189
10 20238
11 20096
12 20215
13 20204
14 20214
15 20214
16 20124
17 20094
18 20223
19 20163
20 20083

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 332 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (11 papers), Advanced Graph Neural Networks (8 papers), Topic Modeling (6 papers), Data Management and Algorithms (5 papers), Web Data Mining and Analysis (5 papers), Machine Learning in Healthcare (5 papers), Expert finding and Q&A systems (4 papers) and Cloud Computing and Resource Management (4 papers). The work is most often cited by research in Information Systems (98 citations), Organic Chemistry (123 citations), Surfaces, Coatings and Films (29 citations), Biomaterials (42 citations) and Artificial Intelligence (97 citations). Xinjun Wang has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Zesheng An, Anqi Zhu, Xiao Wang, Baohua Zhang, Kai Ma, Yue Lv, Zhongmin Yan, Yuliang Shi, Hongchen Wu and Wei Guo. Their work appears in journals such as Knowledge and Information Systems, ACM Transactions on Knowledge Discovery from Data, IEEE Access, Wireless Communications and Mobile Computing and PLoS ONE.

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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