Junhao Gan

703 citations
20 papers · 435 · h-index 7

Impact in

Papers in

Junhao Gan

15 papers receiving 426 citations

Peers

Junhao Gan
Comparison fields: 5 of 66
  • Signal Processing 165
  • Computer Vision and Pattern Recognition 202
  • Artificial Intelligence 233
  • Statistical and Nonlinear Physics 63
  • Computer Networks and Communications 90
Replace Mary Inaba with:
Mary Inaba Japan
Tingyuan Nie China
Olivier Bachem Switzerland
Weiqing Huang China
Cheng Sheng Hong Kong
Mohammad Rezaei Finland
Gereon Frahling Germany
Cong Fu China
Olli Virmajoki Finland
Junhao Gan relative to Mary Inaba Japan Mary Inaba's profile →
Citations per field
00.5×4.7×
Mary Inaba · 1×
Citations per year

Countries citing papers authored by Junhao Gan

Since Specialization
Citations

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

Fields of papers citing papers by Junhao Gan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Junhao Gan, 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 Junhao Gan Line = papers co-authored together Junhao Gan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2015144
2 2012136
3 201749
4 202131
5 201726
6 202114
7 202214
8 20186
9 20224
10 20163
11 20232
12 20232
13 20202
14 20201
15 20201
16 20250
17 20240
18 20240
19 20220
20 20190

About Junhao Gan

Junhao Gan is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Computer Networks and Communications, having authored 20 papers that have together received 435 indexed citations. Recurring topics across this work include Data Management and Algorithms (9 papers), Complex Network Analysis Techniques (6 papers), Advanced Clustering Algorithms Research (4 papers), Algorithms and Data Compression (4 papers), Advanced Graph Neural Networks (3 papers), Recommender Systems and Techniques (3 papers), Advanced Bandit Algorithms Research (2 papers) and Image Retrieval and Classification Techniques (2 papers). The work is most often cited by research in Signal Processing (165 citations), Computer Vision and Pattern Recognition (202 citations), Artificial Intelligence (233 citations), Statistical and Nonlinear Physics (63 citations) and Computer Networks and Communications (90 citations). Junhao Gan has collaborated with scholars based in Australia, China and Hong Kong. Frequent co-authors include Yufei Tao, Jianlin Feng, Wilfred Ng, Qiong Fang, Hao Wu, Zhewei Wei, Rui Zhang, Anthony Wirth, Yunxiang Zhao and Yixin Su. Their work appears in journals such as ACM Transactions on Information Systems, ACM Transactions on Database Systems, The VLDB Journal, Proceedings of the VLDB Endowment and Lecture notes in computer science.

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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