Ting Guo

600 citations
50 papers · 375 indexed · h-index 11
Topics
Advanced Graph Neural Networks (8 papers)Complex Network Analysis Techniques (5 papers)Experimental Behavioral Economics Studies (3 papers)
Journals
SHILAP Revista de lepidopterologíaApplied EnergyPattern Recognition

In The Last Decade

Ting Guo

42 papers receiving 359 citations

Peers

Ting Guo
Comparison fields: 5 of 105
  • Artificial Intelligence 153
  • Statistical and Nonlinear Physics 56
  • Computer Vision and Pattern Recognition 55
  • Information Systems 46
  • Mechanical Engineering 27
Replace Laura Dioşan with:
Laura Dioşan Romania
Behrooz Masoumi Iran
Kejing He China
Ziyan Zhang China
Yongping Du China
Vincent Cohen-Addad United States
Quan Lin China
Debin Liu China
Yawei Zhao China
Ting Guo relative to Laura Dioşan Romania Laura Dioşan's profile →
Citations per field
00.5×2.7×
Laura Dioşan · 1×
Citations per year

Countries citing papers authored by Ting Guo

Since Specialization
Citations

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

Fields of papers citing papers by Ting Guo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ting Guo

This figure shows the co-authorship network connecting the top 25 collaborators of Ting Guo. A scholar is included among the top collaborators of Ting Guo 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 Ting Guo. Ting Guo 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 0
3 1
4 6
5 1
6 11
7 4
8 4
9 3
10 27
11 1
12 11
13 18
14
A conditional Bayesian delay propagation model for large-scale railway traffic networks
3
15 16
16 36
17 7
18 10
19
Infinite hidden semi-Markov modulated interaction point process
2
20 4

About Ting Guo

Ting Guo is a scholar working on General Engineering, Artificial Intelligence and Transportation, having authored 50 papers that have together received 375 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Complex Network Analysis Techniques (5 papers) and Experimental Behavioral Economics Studies (3 papers). The work is most often cited by research in General Engineering (6 citations), Artificial Intelligence (153 citations) and Statistical and Nonlinear Physics (56 citations). Ting Guo has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Xingquan Zhu, Chengqi Zhang, Yang Wang, Shirui Pan, Shuihua Wang‎, Yudong Zhang, Fang Chen, Hongjian Lu, Na Yang and Zefan Wang. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Energy and Pattern Recognition.

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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