Anjin Liu

857 citations
22 papers · 556 indexed · h-index 13
Topics
Data Stream Mining Techniques (18 papers)Machine Learning and Data Classification (10 papers)Advanced Bandit Algorithms Research (6 papers)
Partner nations
AustraliaChina

In The Last Decade

Anjin Liu

21 papers receiving 541 citations

Peers

Anjin Liu
Comparison fields: 5 of 61
  • Artificial Intelligence 480
  • Computer Networks and Communications 107
  • Signal Processing 90
  • Management Science and Operations Research 70
  • Electrical and Electronic Engineering 56
Replace Lena Pietruczuk with:
Lena Pietruczuk Poland
Haitao Yuan China
Aoqian Zhang China
Yang-Geng Fu China
Frieder Ganz United Kingdom
Farah Jemili Tunisia
Jiuyang Tang China
X. Sean Wang United States
Zulong Diao China
Anjin Liu relative to Lena Pietruczuk Poland Lena Pietruczuk's profile →
Citations per field
00.5×7.9×
Lena Pietruczuk · 1×
Citations per year

Countries citing papers authored by Anjin Liu

Since Specialization
Citations

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

Fields of papers citing papers by Anjin Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anjin Liu

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 5
4 6
5 1
6 34
7 19
8 18
9
Confident Anchor-Induced Multi-Source Free Domain Adaptation
28
10 1
11 16
12 25
13 62
14 14
15 5
16 49
17 4
18 60
19 42
20 87

About Anjin Liu

Anjin Liu is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Science Applications, having authored 22 papers that have together received 556 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (18 papers), Machine Learning and Data Classification (10 papers) and Advanced Bandit Algorithms Research (6 papers). The work is most often cited by research in Artificial Intelligence (480 citations), Signal Processing (90 citations) and Management Science and Operations Research (70 citations). Anjin Liu has collaborated with scholars based in Australia and China. Frequent co-authors include Jie Lü, Guangquan Zhang, Yiliao Song, Feng Liu, Kun Wang, Li Xiong, Jiahua Dong, Zhen Fang, Tongliang Liu and Haiyan Lu. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Intelligent Transportation Systems.

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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