Eli Chien

415 citations
14 papers · 89 · h-index 6

Impact in

Papers in

Eli Chien

12 papers receiving 88 citations

Peers

Eli Chien
Comparison fields: 5 of 36
  • Computational Mathematics 11
  • Statistical and Nonlinear Physics 48
  • Artificial Intelligence 39
  • Computer Science Applications 6
  • Computational Theory and Mathematics 13
Replace Madhav Nimishakavi with:
Madhav Nimishakavi India
Hadi Daneshmand Switzerland
Tai Qin United States
Xiyu Zhai United States
Ehsan Hajiramezanali United States
Filip Hanzely Saudi Arabia
Lechao Xiao United States
Nicolas Flammarion United States
Vaishnavh Nagarajan United States
Gary Bécigneul Switzerland
Eli Chien relative to Madhav Nimishakavi India Madhav Nimishakavi's profile →
Citations per field
00.5×
Madhav Nimishakavi · 1×
Citations per year

Countries citing papers authored by Eli Chien

Since Specialization
Citations

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

Fields of papers citing papers by Eli Chien

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Eli Chien, 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 Eli Chien Line = papers co-authored together Eli Chien links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1
Community Detection in Hypergraphs: Optimal Statistical Limit and Efficient Algorithms
201821
2 201919
3 201716
4 202310
5 20247
6 20246
7 20214
8
$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries
20192
9 20231
10 20221
11 20201
12 20221
13 20240
14 20240

About Eli Chien

Eli Chien is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computational Theory and Mathematics, Computer Networks and Communications and Biophysics, having authored 14 papers that have together received 89 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (4 papers), Complex Network Analysis Techniques (4 papers), Topological and Geometric Data Analysis (3 papers), Advanced Clustering Algorithms Research (2 papers), Cell Image Analysis Techniques (2 papers), SARS-CoV-2 and COVID-19 Research (1 paper), Medical Image Segmentation Techniques (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Computational Mathematics (11 citations), Statistical and Nonlinear Physics (48 citations), Artificial Intelligence (39 citations), Computer Science Applications (6 citations) and Computational Theory and Mathematics (13 citations). Eli Chien has collaborated with scholars based in United States, Taiwan and Jamaica. Frequent co-authors include I-Hsiang Wang, Olgica Milenković, Chao Pan, Zezhou Cheng, Xiang Yue, Tianhao Wang, Pan Li, Minxin Du, David Z. Pan and Zhongyuan Zhao. Their work appears in journals such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Transactions on Information Theory, Knowledge and Information Systems, International Conference on Artificial Intelligence and Statistics and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

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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