Cheng–Te Li

2.8k citations
132 papers · 1.4k indexed · h-index 22

Cheng–Te Li

125 papers receiving 1.4k citations

Peers

Cheng–Te Li
Comparison fields: 5 of 103
  • Transportation 213
  • Statistical and Nonlinear Physics 301
  • Computer Science Applications 129
  • Artificial Intelligence 678
  • Information Systems 457
Replace Zongyang Ma with:
Zongyang Ma Singapore
Charu C. Aggarwal United States
Senjuti Basu Roy United States
Ju Fan China
Zhixu Li China
Adam Marcus United States
Gisele L. Pappa Brazil
Tieyun Qian China
Bee-Chung Chen United States
Cheng–Te Li relative to Zongyang Ma Singapore Zongyang Ma's profile →
Citations per field
00.5×4.5×
Zongyang Ma · 1×
Citations per year

Countries citing papers authored by Cheng–Te Li

Since Specialization
Citations

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

Fields of papers citing papers by Cheng–Te Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cheng–Te Li. 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 Cheng–Te Li. The network helps show where Cheng–Te Li may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20240
3 20240
4 20241
5 20231
6 20231
7 20232
8 20223
9 20223
10 202150
11 202127
12 20163
13
Measuring and recommending time-sensitive routes from location-based data
20152
14
Opportunities or risks to reduce labor in crowdsourcing translation? characterizing cost versus quality via a pagerank-HITS hybrid model
20154
15 20142
16 201262
17 20126
18
MemeTube: A Sentiment-based Audiovisual System for Analyzing and Displaying Microblog Messages
20118
19 20110
20 201088

About Cheng–Te Li

Cheng–Te Li is a scholar working on Transportation, Statistical and Nonlinear Physics, Signal Processing, Artificial Intelligence and Information Systems, having authored 132 papers that have together received 1.4k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (43 papers), Human Mobility and Location-Based Analysis (38 papers), Advanced Graph Neural Networks (28 papers), Recommender Systems and Techniques (18 papers), Topic Modeling (14 papers), Data Management and Algorithms (13 papers), Opinion Dynamics and Social Influence (12 papers) and Spam and Phishing Detection (11 papers). The work is most often cited by research in Transportation (213 citations), Statistical and Nonlinear Physics (301 citations), Computer Science Applications (129 citations), Artificial Intelligence (678 citations) and Information Systems (457 citations). Cheng–Te Li has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Man-Kwan Shan, Shou-De Lin, Hsun-Ping Hsieh, Chih-Yao Chen, Mi-Yen Yeh, Jun Pang, Rui Yan, Zhiqiang Zhong, Meeyoung Cha and Ming Zhang. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, Knowledge and Information Systems, IEEE Transactions on Knowledge and Data Engineering, Information Sciences and Applied Sciences.

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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