Canrong Tian

943 citations
54 papers · 722 indexed · h-index 17

Canrong Tian

53 papers receiving 707 citations

Peers

Canrong Tian
Comparison fields: 5 of 56
  • Modeling and Simulation 242
  • Public Health, Environmental and Occupational Health 548
  • Computer Networks and Communications 299
  • Statistical and Nonlinear Physics 126
  • Genetics 285
Replace Zhanji Gui with:
Zhanji Gui China
Zohreh Eskandari Iran
Xuebing Zhang China
G. Gambino Italy
Yongzhen Pei China
Fengqi Yi China
Uttam Ghosh India
Lakshmi Narayan Guin India
Gaihui Guo China
Marcus R. Garvie Canada
Canrong Tian relative to Zhanji Gui China Zhanji Gui's profile →
Citations per field
00.5×3.2×
Zhanji Gui · 1×
Citations per year

Countries citing papers authored by Canrong Tian

Since Specialization
Citations

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

Fields of papers citing papers by Canrong Tian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 23 scholars most cited alongside Canrong Tian, 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 Canrong Tian Line = papers co-authored together Canrong Tian links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20251
3 20241
4 20245
5 20244
6 20241
7 20231
8 20228
9 202213
10 202043
11 20189
12 201721
13 201714
14 20159
15 201519
16 201443
17 20122
18 201116
19 200941
20
GENERALIZED KP EQUATION AND MIURA TRANSFORMATION
19890

About Canrong Tian

Canrong Tian is a scholar working on Modeling and Simulation, Public Health, Environmental and Occupational Health and Computer Networks and Communications, having authored 54 papers that have together received 722 indexed citations. Recurring topics across this work include Mathematical and Theoretical Epidemiology and Ecology Models (43 papers), Nonlinear Dynamics and Pattern Formation (23 papers), Evolution and Genetic Dynamics (19 papers), Ecosystem dynamics and resilience (12 papers), Mathematical Biology Tumor Growth (10 papers), Fractional Differential Equations Solutions (5 papers), Opinion Dynamics and Social Influence (5 papers) and Evolutionary Game Theory and Cooperation (5 papers). The work is most often cited by research in Modeling and Simulation (242 citations), Public Health, Environmental and Occupational Health (548 citations) and Computer Networks and Communications (299 citations). Canrong Tian has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Lai Zhang, Zhigui Lin, Shigui Ruan, Ricardo Ruíz-Baier, Zhi Ling, Zuhan Liu, Qunying Zhang, Michael Pedersen, Mostafa Bendahmane and Ling Lin. Their work appears in journals such as Journal of Computational Physics, Journal of the Franklin Institute and Physica A Statistical Mechanics and its Applications.

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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