Debo Cheng

2.4k total citations · 2 hit papers
53 papers, 1.5k citations indexed

About

Debo Cheng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics and Probability. According to data from OpenAlex, Debo Cheng has authored 53 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 10 papers in Statistics and Probability. Recurrent topics in Debo Cheng's work include Face and Expression Recognition (17 papers), Advanced Graph Neural Networks (15 papers) and Advanced Causal Inference Techniques (10 papers). Debo Cheng is often cited by papers focused on Face and Expression Recognition (17 papers), Advanced Graph Neural Networks (15 papers) and Advanced Causal Inference Techniques (10 papers). Debo Cheng collaborates with scholars based in China, Australia and Japan. Debo Cheng's co-authors include Shichao Zhang, Ming Zong, Zhenyun Deng, Xiaofeng Zhu, Xiaoshu Zhu, Xuelong Li, Xuelian Deng, Rongyao Hu, Wei He and Jingkuan Song and has published in prestigious journals such as ACM Computing Surveys, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.

In The Last Decade

Debo Cheng

46 papers receiving 1.4k citations

Hit Papers

Efficient kNN classification algorithm for big data 2016 2026 2019 2022 2016 2017 100 200 300 400
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 kNN classification algorithm for big data
    2016 430 citations Zhenyun Deng, Debo Cheng et al. Neurocomputing profile →
  2. Learning k for kNN Classification
    2017 364 citations Shichao Zhang, Debo Cheng et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Debo Cheng China 14 708 398 188 118 97 53 1.5k
Diyar Qader Zeebaree Iraq 14 646 0.9× 360 0.9× 100 0.5× 113 1.0× 115 1.2× 25 1.3k
Yilin Yan United States 10 635 0.9× 352 0.9× 122 0.6× 129 1.1× 84 0.9× 23 1.3k
Salem Alelyani Saudi Arabia 18 743 1.0× 272 0.7× 248 1.3× 155 1.3× 137 1.4× 35 1.7k
Samira Pouyanfar United States 15 778 1.1× 510 1.3× 161 0.9× 179 1.5× 60 0.6× 28 1.8k
Karla Brkić Croatia 13 460 0.6× 363 0.9× 130 0.7× 77 0.7× 94 1.0× 38 1.2k
Md Ekrim Hossin Malaysia 5 681 1.0× 238 0.6× 182 1.0× 147 1.2× 76 0.8× 18 1.9k
Mahmoud Y. Shams Egypt 26 529 0.7× 252 0.6× 139 0.7× 93 0.8× 78 0.8× 70 1.7k
Harry Zhang Canada 13 745 1.1× 223 0.6× 279 1.5× 130 1.1× 113 1.2× 29 1.4k
Ji Feng China 10 806 1.1× 375 0.9× 131 0.7× 123 1.0× 147 1.5× 22 1.6k

Countries citing papers authored by Debo Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Debo Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debo Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Debo Cheng. A scholar is included among the top collaborators of Debo Cheng 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 Debo Cheng. Debo Cheng 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.
Zhang, Guixian, et al.. (2025). Latent Representation Learning for Attributed Graph Anomaly Detection. ACM Transactions on Knowledge Discovery from Data. 19(7). 1–22.
2.
Cheng, Debo, et al.. (2025). Leveraging long-range nodes in multi-view graph contrastive learning. Information Fusion. 122. 103186–103186. 1 indexed citations
3.
Zhang, Guixian, et al.. (2025). A multi-view graph neural network with subgraph variational autoencoder for class-Imbalanced node classification. Knowledge-Based Systems. 334. 115081–115081.
4.
Hu, Yuxuan, Debo Cheng, Jiuyong Li, et al.. (2025). Multi-Cause Deconfounding for Recommender Systems with Latent Confounders. Knowledge-Based Systems. 329. 114345–114345. 1 indexed citations
5.
Cheng, Debo, et al.. (2025). Toward fair graph neural networks via dual-teacher knowledge distillation. Neural Networks. 194. 108184–108184.
6.
Li, Chen, Ye Zhu, Yang Cao, et al.. (2024). Mining area skyline objects from map-based big data using Apache Spark framework. Array. 25. 100373–100373. 1 indexed citations
7.
8.
Cheng, Debo, et al.. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11480–11488. 2 indexed citations
9.
Li, Chengyu, Debo Cheng, Guixian Zhang, & Shichao Zhang. (2024). Contrastive learning for fair graph representations via counterfactual graph augmentation. Knowledge-Based Systems. 305. 112635–112635. 6 indexed citations
10.
Li, Chen, et al.. (2024). Ripple Knowledge Graph Convolutional Networks for Recommendation Systems. UniSA Research Outputs Repository (University of South Australia). 21(3). 481–494. 10 indexed citations
11.
Cheng, Debo, et al.. (2024). Identifying local useful information for attribute graph anomaly detection. Neurocomputing. 617. 128900–128900. 5 indexed citations
12.
Mayer, Wolfgang, et al.. (2024). Domain Ontology-Driven Knowledge Graph Generation from Text. 1(4). 1–18.
13.
14.
Cheng, Debo, et al.. (2024). Disentangled Representation Learning for Causal Inference With Instruments. IEEE Transactions on Neural Networks and Learning Systems. 36(8). 14078–14091. 3 indexed citations
15.
Zhang, Guixian, et al.. (2024). Disentangled contrastive learning for fair graph representations. Neural Networks. 181. 106781–106781. 15 indexed citations
16.
Cheng, Debo, et al.. (2023). LRAGAD: Local Information Recognition for Attribute Graph Anomaly Detection. 2. 997–1001. 1 indexed citations
17.
Cheng, Debo, et al.. (2023). Disentangled Representation for Causal Mediation Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10666–10674. 5 indexed citations
18.
Cheng, Debo, et al.. (2023). Causal Inference with Conditional Instruments Using Deep Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7122–7130. 8 indexed citations
19.
Cheng, Debo, Jiuyong Li, Lin Liu, et al.. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery. 36(3). 1174–1196. 6 indexed citations
20.
Xu, Shihong, et al.. (2020). Application of ToF-SIMS to predict contact angles of pyrite particles. Minerals Engineering. 147. 106168–106168. 16 indexed citations

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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