Shousheng Liu

2.9k citations
85 papers · 2.0k indexed · 1 hit paper · h-index 20

Shousheng Liu

83 papers receiving 1.9k citations

Hit Papers

Generalized aggregation operators for intuitionistic fuzz...4042009202620142020100200300400

Peers

Shousheng Liu
Comparison fields: 5 of 155
  • Management Science and Operations Research 590
  • Hepatology 292
  • Statistics and Probability 211
  • Epidemiology 684
  • Endocrinology, Diabetes and Metabolism 257
Replace Claire O’Brien with:
Claire O’Brien United Kingdom
Jung‐Fa Tsai Taiwan
Sigurður Ólafsson United States
Christopher O’Brien United States
Weilong Liu China
Konstantinos Anagnostopoulos Greece
Yutaka Takahashi Japan
Chunyan Duan China
Qin Yu China
Turgay Ayer United States
Shousheng Liu relative to Claire O’Brien United Kingdom Claire O’Brien's profile →
Citations per field
00.5×5.5×
Claire O’Brien · 1×
Citations per year

Countries citing papers authored by Shousheng Liu

Since Specialization
Citations

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

Fields of papers citing papers by Shousheng Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20241
2 20247
3 20242
4 20230
5 20231
6 20222
7 20223
8 20214
9 20207
10 20208
11 202026
12 20198
13 201911
14 201816
15 201813
16 20187
17 201813
18 201814
19 20174
20
Multipopulation Parallel Genetic Algorithm Based on Even Partition
20032

About Shousheng Liu

Shousheng Liu is a scholar working on Hepatology, Epidemiology and Management Science and Operations Research, having authored 85 papers that have together received 2.0k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (42 papers), Multi-Criteria Decision Making (12 papers), Lipid metabolism and disorders (12 papers), Colorectal Cancer Treatments and Studies (12 papers), Diet, Metabolism, and Disease (9 papers), Hepatitis C virus research (9 papers), Liver Disease and Transplantation (8 papers) and Hepatocellular Carcinoma Treatment and Prognosis (8 papers). The work is most often cited by research in Management Science and Operations Research (590 citations), Hepatology (292 citations) and Statistics and Probability (211 citations). Shousheng Liu has collaborated with scholars based in China, Japan and Hong Kong. Frequent co-authors include Zeshui Xu, Hua Zhao, Yongning Xin, Mingfang Ni, Hamido Fujita, Hai Wang, Shui-Xian Du, Quanjiang Dong, Jie Zhang and Xuefeng Ma. Their work appears in journals such as Scientific Reports, Endocrinology and IEEE Access.

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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