Tieke He

706 citations
39 papers · 408 indexed · h-index 12

Impact in

  • Software top 5%
    • Software Testing and Debugging Techniques
    • Software Reliability and Analysis Research
    • Software Engineering Research
    • Recommender Systems and Techniques

Papers in

    • Software Testing and Debugging Techniques 8
    • Software Engineering Research 12
    • Recommender Systems and Techniques 5
    • Web Data Mining and Analysis 4

Tieke He

34 papers receiving 395 citations

Peers

Tieke He
Comparison fields: 5 of 58
  • Software 87
  • Information Systems 229
  • Computer Science Applications 41
  • Artificial Intelligence 187
  • Signal Processing 57
Replace Inah Omoronyia with:
Inah Omoronyia United Kingdom
Maliheh Izadi Netherlands
Roberto de Pinho Brazil
Tom Chao Zhou Hong Kong
Irene Garrigós Spain
Prasang Upadhyaya United States
Massila Kamalrudin Malaysia
Hicham G. Elmongui United States
Tieke He relative to Inah Omoronyia United Kingdom Inah Omoronyia's profile →
Citations per field
00.5×9.9×
Inah Omoronyia · 1×
Citations per year

Countries citing papers authored by Tieke He

Since Specialization
Citations

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

Fields of papers citing papers by Tieke He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
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8 202334
9 202360
10 202235
11 20202
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13 20209
14 20191
15 20192
16 20185
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18 201843
19 201718
20
Comparing Collaborative Filtering Methods Based on User-Topic Ratings.
20131

About Tieke He

Tieke He is a scholar working on Software, Information Systems, Artificial Intelligence, General Social Sciences and Signal Processing, having authored 39 papers that have together received 408 indexed citations. Recurring topics across this work include Software Engineering Research (12 papers), Topic Modeling (9 papers), Software Testing and Debugging Techniques (8 papers), Software System Performance and Reliability (7 papers), Advanced Graph Neural Networks (7 papers), Artificial Intelligence in Law (6 papers), Recommender Systems and Techniques (5 papers) and Web Data Mining and Analysis (4 papers). The work is most often cited by research in Software (87 citations), Information Systems (229 citations), Computer Science Applications (41 citations), Artificial Intelligence (187 citations) and Signal Processing (57 citations). Tieke He has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Hongzhi Yin, Zhenyu Chen, Quoc Viet Hung Nguyen, He Jiang, Xin Chen, Xiaochen Li, Fangzhao Wu, Shijie Zhang, Hao Lian and Chunrong Fang. Their work appears in journals such as Neural Networks, ACM Transactions on Intelligent Systems and Technology, Empirical Software Engineering, IEEE Transactions on Software Engineering and ACM Transactions on Internet Technology.

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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