X. D. Zhang

1.1k total citations · 1 hit paper
30 papers, 557 citations indexed

About

X. D. Zhang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, X. D. Zhang has authored 30 papers receiving a total of 557 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Artificial Intelligence, 7 papers in Statistical and Nonlinear Physics and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in X. D. Zhang's work include Advanced Graph Neural Networks (18 papers), Complex Network Analysis Techniques (7 papers) and Explainable Artificial Intelligence (XAI) (5 papers). X. D. Zhang is often cited by papers focused on Advanced Graph Neural Networks (18 papers), Complex Network Analysis Techniques (7 papers) and Explainable Artificial Intelligence (XAI) (5 papers). X. D. Zhang collaborates with scholars based in United States, China and Singapore. X. D. Zhang's co-authors include Wei Cheng, Dongsheng Luo, Jingchao Ni, Bo Zong, Wenchao Yu, Dongkuan Xu, Siyu Huang, Yihang Yin, Haifeng Chen and Suhang Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Intelligent Systems and Technology.

In The Last Decade

X. D. Zhang

27 papers receiving 549 citations

Hit Papers

align trajectories

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.

Learning to Drop: Robust Graph Neural Network via Topological Denoising

2021 156 citations Dongsheng Luo, Wei Cheng et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
X. D. Zhang United States	12	456	111	109	109	48	30	557
Chenyi Zhuang Japan	9	295 0.6×	98 0.9×	138 1.3×	120 1.1×	34 0.7×	29	469
Aravind Sankar United States	7	416 0.9×	162 1.5×	88 0.8×	185 1.7×	40 0.8×	13	536
Giang Nguyen United States	5	332 0.7×	78 0.7×	52 0.5×	204 1.9×	50 1.0×	8	412
Lun Du China	15	541 1.2×	271 2.4×	99 0.9×	203 1.9×	39 0.8×	51	755
Yanhong Wu China	5	284 0.6×	68 0.6×	51 0.5×	135 1.2×	27 0.6×	7	351
Shuyi Ji China	6	265 0.6×	128 1.2×	123 1.1×	45 0.4×	46 1.0×	7	469
Deyu Bo China	4	441 1.0×	137 1.2×	139 1.3×	150 1.4×	30 0.6×	5	544
Qingqing Long China	9	216 0.5×	51 0.5×	89 0.8×	69 0.6×	38 0.8×	19	329
Quanyu Dai China	16	622 1.4×	296 2.7×	163 1.5×	141 1.3×	43 0.9×	44	829
Sungsu Lim South Korea	13	188 0.4×	74 0.7×	68 0.6×	188 1.7×	45 0.9×	49	455

Countries citing papers authored by X. D. Zhang

Since Specialization

Citations

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

Fields of papers citing papers by X. D. Zhang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by X. D. Zhang. 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 X. D. Zhang. The network helps show where X. D. Zhang may publish in the future.

Co-authorship network of co-authors of X. D. Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of X. D. Zhang. A scholar is included among the top collaborators of X. D. Zhang 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 X. D. Zhang. X. D. Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Luo, Dongsheng, et al.. (2025). DyExplainer: Self-explainable Dynamic Graph Neural Network with Sparse Attentions. ACM Transactions on Knowledge Discovery from Data. 19(4). 1–21. 4 indexed citations

2.

Dai, Enyan, et al.. (2025). Stealing Training Graphs from Graph Neural Networks. 777–788.

3.

Zhao, Tianxiang, X. D. Zhang, & Suhang Wang. (2024). Disambiguated Node Classification with Graph Neural Networks. arXiv (Cornell University). 914–923. 4 indexed citations

4.

Luo, Dongsheng, Tianxiang Zhao, Wei Cheng, et al.. (2024). Towards Inductive and Efficient Explanations for Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(8). 5245–5259. 6 indexed citations

5.

Dai, Enyan, et al.. (2024). Shape-aware Graph Spectral Learning. 2692–2701. 2 indexed citations

6.

Zhao, Tianxiang, Wenchao Yu, Suhang Wang, et al.. (2024). Interpretable Imitation Learning with Dynamic Causal Relations. 967–975.

7.

Zhang, X. D., et al.. (2024). Stock price nowcasting and forecasting with deep learning. Journal of Intelligent Information Systems. 63(2). 639–656. 4 indexed citations

8.

Zhao, Tianxiang, Wenchao Yu, Suhang Wang, et al.. (2023). Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations. arXiv (Cornell University). 3513–3524. 1 indexed citations

9.

Zhang, X. D., A. Adam Ding, & Yunsi Fei. (2023). Deep-Learning Model Extraction Through Software-Based Power Side-Channel. 1–9. 2 indexed citations

10.

Wang, Tianchun, Wei Cheng, Dongsheng Luo, et al.. (2022). Personalized Federated Learning via Heterogeneous Modular Networks. 1197–1202. 7 indexed citations

11.

Xu, Junjie, Enyan Dai, X. D. Zhang, & Suhang Wang. (2022). HP-GMN: Graph Memory Networks for Heterophilous Graphs. 1263–1268. 8 indexed citations

12.

Zhao, Tianxiang, X. D. Zhang, & Suhang Wang. (2022). Exploring Edge Disentanglement for Node Classification. Proceedings of the ACM Web Conference 2022. 1028–1036. 20 indexed citations

13.

Luo, Dongsheng, Wei Cheng, Wenchao Yu, et al.. (2021). Learning to Drop: Robust Graph Neural Network via Topological Denoising. 779–787. 156 indexed citations breakdown →

14.

Xu, Dongkuan, Junjie Liang, Wei Cheng, et al.. (2021). Transformer-Style Relational Reasoning with Dynamic Memory Updating for Temporal Network Modeling. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 4546–4554. 11 indexed citations

15.

Zhang, X. D. & Marinka Žitnik. (2020). GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. Neural Information Processing Systems. 33. 9263–9275. 8 indexed citations

16.

Xu, Dongkuan, Wei Cheng, Bo Zong, et al.. (2020). Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence. 34(2). 1395–1402. 31 indexed citations

17.

Ni, Jingchao, Shiyu Chang, Xiao Liu, et al.. (2018). Co-Regularized Deep Multi-Network Embedding. 469–478. 43 indexed citations

18.

Ni, Jingchao, Wei Cheng, Wei Fan, & X. D. Zhang. (2017). ComClus: A Self-Grouping Framework for Multi-Network Clustering. IEEE Transactions on Knowledge and Data Engineering. 30(3). 435–448. 11 indexed citations

19.

Ni, Jingchao, Hongliang Fei, Wei Fan, & X. D. Zhang. (2017). Cross-Network Clustering and Cluster Ranking for Medical Diagnosis. 163–166. 6 indexed citations

20.

Cheng, Wei, Xiaochuan Ni, Jian-Tao Sun, et al.. (2011). Measuring Opinion Relevance in Latent Topic Space. 3206. 323–330. 2 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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