Chaobo He

801 total citations · 1 hit paper
57 papers, 514 citations indexed

About

Chaobo He is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Information Systems. According to data from OpenAlex, Chaobo He has authored 57 papers receiving a total of 514 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 29 papers in Statistical and Nonlinear Physics and 18 papers in Information Systems. Recurrent topics in Chaobo He's work include Complex Network Analysis Techniques (29 papers), Advanced Graph Neural Networks (29 papers) and Recommender Systems and Techniques (10 papers). Chaobo He is often cited by papers focused on Complex Network Analysis Techniques (29 papers), Advanced Graph Neural Networks (29 papers) and Recommender Systems and Techniques (10 papers). Chaobo He collaborates with scholars based in China, United Kingdom and United States. Chaobo He's co-authors include Yong Tang, Xiang Fei, Hanchao Li, Zeng Hu, Chang‐Dong Wang, Dong Huang, Miranda Lee Pao, Shuangyin Liu, Junwei Cheng and Qimai Chen and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Chaobo He

51 papers receiving 497 citations

Hit Papers

Efficient Multi-View Clustering via Unified and Discrete ... 2023 2026 2024 2025 2023 25 50 75 100
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning
    2023 112 citations Dong Huang, Chang‐Dong Wang et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Chaobo He China 11 296 234 161 87 54 57 514
Joyce Jiyoung Whang South Korea 10 314 1.1× 386 1.6× 88 0.5× 79 0.9× 128 2.4× 29 578
Quanyu Dai China 16 622 2.1× 141 0.6× 163 1.0× 296 3.4× 65 1.2× 44 829
Rushed Kanawati France 12 255 0.9× 337 1.4× 34 0.2× 106 1.2× 109 2.0× 31 499
Elahe Nasiri Iran 9 323 1.1× 285 1.2× 69 0.4× 82 0.9× 103 1.9× 9 568
Fanghua Ye China 15 488 1.6× 329 1.4× 144 0.9× 108 1.2× 140 2.6× 45 757
Xiaolin Jia China 9 202 0.7× 159 0.7× 39 0.2× 63 0.7× 62 1.1× 35 355
Yinglong Xia United States 13 326 1.1× 85 0.4× 96 0.6× 87 1.0× 75 1.4× 53 437
Shaohua Fan China 8 571 1.9× 178 0.8× 138 0.9× 247 2.8× 99 1.8× 14 714
Houye Ji China 8 510 1.7× 85 0.4× 112 0.7× 159 1.8× 38 0.7× 10 593
Deyu Bo China 4 441 1.5× 150 0.6× 139 0.9× 137 1.6× 61 1.1× 5 544

Countries citing papers authored by Chaobo He

Since Specialization
Citations

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

Fields of papers citing papers by Chaobo He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chaobo He

This figure shows the co-authorship network connecting the top 25 collaborators of Chaobo He. A scholar is included among the top collaborators of Chaobo He 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 Chaobo He. Chaobo He 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
1.
He, Chaobo, et al.. (2025). HCKGL: Hyperbolic collaborative knowledge graph learning for recommendation. Neurocomputing. 634. 129808–129808. 2 indexed citations
2.
Cheng, Junwei, et al.. (2025). When graph neural networks meet deep nonnegative matrix factorization: An encoder and decoder-like method for community detection. Expert Systems with Applications. 271. 126676–126676. 2 indexed citations
3.
4.
Huang, Dong, et al.. (2025). Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning. IEEE Signal Processing Letters. 32. 1850–1854. 2 indexed citations
5.
Cheng, Junwei, et al.. (2025). Rethinking Variational Bayes in Community Detection From Graph Signal Perspective. IEEE Transactions on Knowledge and Data Engineering. 37(5). 2903–2917.
6.
He, Chaobo, et al.. (2024). Signed graph embedding via multi-order neighborhood feature fusion and contrastive learning. Neural Networks. 182. 106897–106897. 2 indexed citations
7.
He, Chaobo, Ziliang Chen, Guanliang Chen, et al.. (2024). Reason-and-Execute Prompting: Enhancing Multi-Modal Large Language Models for Solving Geometry Questions. 6959–6968. 1 indexed citations
8.
Hu, Zeng, et al.. (2024). Orthogonal Frequency Division Multiplexing With Generalized Joint Index Modulation. IEEE Open Journal of the Communications Society. 6. 3004–3017.
9.
Cheng, Junwei, et al.. (2024). Unveiling community structures in static networks through graph variational Bayes with evolution information. Neurocomputing. 576. 127349–127349. 1 indexed citations
10.
Guan, Quanlong, et al.. (2024). Explainable exercise recommendation with knowledge graph. Neural Networks. 183. 106954–106954. 7 indexed citations
11.
Guan, Quanlong, et al.. (2024). Generating Privacy-preserving Educational Data Records with Diffusion Model. 806–809. 1 indexed citations
12.
He, Chaobo, et al.. (2024). Detecting communities with multiple topics in attributed networks via self-supervised adaptive graph convolutional network. Information Fusion. 105. 102254–102254. 7 indexed citations
13.
He, Chaobo, et al.. (2023). Community preserving adaptive graph convolutional networks for link prediction in attributed networks. Knowledge-Based Systems. 272. 110589–110589. 14 indexed citations
14.
He, Chaobo, et al.. (2023). Community Detection Based on Directed Weighted Signed Graph Convolutional Networks. IEEE Transactions on Network Science and Engineering. 11(2). 1642–1654. 8 indexed citations
15.
Huang, Dong, et al.. (2023). Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning. IEEE Transactions on Neural Networks and Learning Systems. 35(8). 11436–11447. 112 indexed citations breakdown →
16.
He, Chaobo, et al.. (2023). DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning. World Wide Web. 26(5). 2471–2493. 5 indexed citations
17.
He, Chaobo, et al.. (2020). Similarity preserving overlapping community detection in signed networks. Future Generation Computer Systems. 116. 275–290. 13 indexed citations
18.
Zhao, Gansen, et al.. (2016). Large-scale topic community mining based on distributed nonnegative matrix factorization. Scientia Sinica Informationis. 46(6). 714–728. 1 indexed citations
19.
He, Chaobo, et al.. (2012). Collaborative Recommendation Model Based on Social Network and Its Application. Journal of Convergence Information Technology. 7(2). 253–261. 4 indexed citations
20.
He, Chaobo & Qimai Chen. (2011). Rough set analysis model for correlation between courses and its application. Computer Engineering and Applications Journal. 47(27). 233–235.

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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