Liang Hou

405 citations
13 papers · 119 indexed · h-index 5
Topics
Advanced Neural Network Applications (4 papers)Advanced Graph Neural Networks (3 papers)Generative Adversarial Networks and Image Synthesis (3 papers)
Partner nations
ChinaPolandSingapore

In The Last Decade

Liang Hou

12 papers receiving 116 citations

Peers

Liang Hou
Comparison fields: 5 of 38
  • Artificial Intelligence 68
  • Computer Vision and Pattern Recognition 35
  • Statistical and Nonlinear Physics 35
  • Molecular Biology 20
  • Information Systems 13
Replace Christopher Fifty with:
Christopher Fifty United States
Vikram Nitin United States
Madhav Nimishakavi India
Ofer Meshi United States
Tai Qin United States
Seyed Mehran Kazemi Canada
Hadi Daneshmand Switzerland
Ehsan Hajiramezanali United States
Keyulu Xu United States
Naganand Yadati India
Liang Hou relative to Christopher Fifty United States Christopher Fifty's profile →
Citations per field
00.5×10×20×30×35×
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Citations per year

Countries citing papers authored by Liang Hou

Since Specialization
Citations

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

Fields of papers citing papers by Liang Hou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liang Hou

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

All Works

13 of 13 papers shown
#WorkIndexed citations
1 2
2 8
3 9
4 1
5 0
6 4
7 1
8 2
9 1
10 16
11 43
12 31
13 1

About Liang Hou

Liang Hou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 13 papers that have together received 119 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Advanced Graph Neural Networks (3 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (35 citations), Artificial Intelligence (68 citations) and Computer Vision and Pattern Recognition (35 citations). Liang Hou has collaborated with scholars based in China, Poland and Singapore. Frequent co-authors include Xueqi Cheng, Junjie Huang, Huawei Shen, Huawei Shen, Bingbing Xu, Jinhua Gao, Changhu Wang, Lei Huang, Zehuan Yuan and Jiahui Wang. Their work appears in journals such as IEEE Access, European Journal of Medicinal Chemistry and IEEE Transactions on Knowledge and Data Engineering.

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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