Qingqing Long

1.4k total citations · 1 hit paper
19 papers, 329 citations indexed

About

Qingqing Long is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Qingqing Long has authored 19 papers receiving a total of 329 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 8 papers in Statistical and Nonlinear Physics and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Qingqing Long's work include Advanced Graph Neural Networks (10 papers), Complex Network Analysis Techniques (8 papers) and Recommender Systems and Techniques (3 papers). Qingqing Long is often cited by papers focused on Advanced Graph Neural Networks (10 papers), Complex Network Analysis Techniques (8 papers) and Recommender Systems and Techniques (3 papers). Qingqing Long collaborates with scholars based in China, Hong Kong and United States. Qingqing Long's co-authors include Guojie Song, Yilun Jin, Wei Lin, Zheng Fang, Yifang Qin, Yusheng Zhao, Yi Li, Fang Sun, Xiao Luo and Zhiping Xiao and has published in prestigious journals such as Nature Communications, Neural Networks and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

Qingqing Long

18 papers receiving 320 citations

Hit Papers

A Comprehensive Survey on Deep Graph Representation Learning 2024 2026 2025 2024 40 80 120
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. A Comprehensive Survey on Deep Graph Representation Learning
    2024 138 citations Wei Ju, Zheng Fang et al. Neural Networks profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingqing Long China 9 216 89 69 51 38 19 329
Yiwei Wang Singapore 7 203 0.9× 46 0.5× 55 0.8× 50 1.0× 14 0.4× 14 262
Bingbing Xu China 8 197 0.9× 65 0.7× 44 0.6× 49 1.0× 17 0.4× 18 303
Tianle Cai China 4 157 0.7× 73 0.8× 30 0.4× 31 0.6× 53 1.4× 7 278
X. D. Zhang United States 12 456 2.1× 109 1.2× 109 1.6× 111 2.2× 48 1.3× 30 557
Ziyue Qiao China 8 173 0.8× 46 0.5× 24 0.3× 39 0.8× 29 0.8× 24 246
Chengxuan Ying China 1 143 0.7× 65 0.7× 28 0.4× 28 0.5× 51 1.3× 2 248
Yuanfei Dai China 7 279 1.3× 78 0.9× 43 0.6× 57 1.1× 73 1.9× 18 412
Cheng Ji China 8 254 1.2× 53 0.6× 39 0.6× 49 1.0× 22 0.6× 18 370

Countries citing papers authored by Qingqing Long

Since Specialization
Citations

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

Fields of papers citing papers by Qingqing Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingqing Long

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

All Works

19 of 19 papers shown
1.
Hoeft, Konrad, Tore Bleckwehl, David Schumacher, et al.. (2024). Label-free single-cell RNA multiplexing leveraging genetic variability. Nature Communications. 15(1). 10612–10612. 2 indexed citations
2.
Ju, Wei, Zheng Fang, Yiyang Gu, et al.. (2024). A Comprehensive Survey on Deep Graph Representation Learning. Neural Networks. 173. 106207–106207. 138 indexed citations breakdown →
3.
4.
Zhang, Peiyan, et al.. (2024). Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective. arXiv (Cornell University). 4328–4339. 6 indexed citations
5.
Ju, Wei, Yifan Wang, Zhiping Xiao, et al.. (2024). Learning Knowledge-diverse Experts for Long-tailed Graph Classification. ACM Transactions on Knowledge Discovery from Data. 19(2). 1–24. 1 indexed citations
6.
Wang, Yidong, Qingqing Long, Lu Wang, et al.. (2024). How Do Large Language Models Understand Genes and Cells. ACM Transactions on Intelligent Systems and Technology. 16(6). 1–16. 1 indexed citations
7.
Long, Qingqing, Wenhao Liu, Fang Chen, et al.. (2024). Hierarchical Graph Transformer With Contrastive Learning for Gene Regulatory Network Inference. IEEE Journal of Biomedical and Health Informatics. 29(1). 690–699.
8.
Li, Wei, Yang Cong, Qingqing Long, et al.. (2022). Caffeic Acid Phenethyl Ester Inhibits Ubiquitination and Degradationof p53 and Blocks Cervical Cancer Cell Growth. Current Molecular Medicine. 23(9). 960–970. 7 indexed citations
9.
Zhang, Xiang, Feng Li, Pengjie Wang, et al.. (2022). Joint Optimization of Ad Ranking and Creative Selection. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2341–2346. 2 indexed citations
10.
Fang, Zheng, et al.. (2022). Polarized Graph Neural Networks. Proceedings of the ACM Web Conference 2022. 1404–1413. 20 indexed citations
11.
Long, Qingqing, et al.. (2021). Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1204–1214. 21 indexed citations
12.
13.
Chen, Xu, Lun Du, Mengyuan Chen, et al.. (2021). Fast Hierarchy Preserving Graph Embedding via Subspace Constraints. 3580–3584. 3 indexed citations
14.
Long, Qingqing, et al.. (2021). HGK-GNN. 1129–1138. 16 indexed citations
15.
Wang, Junshan, Ziyao Li, Qingqing Long, et al.. (2020). Learning Node Representations from Noisy Graph Structures. 1310–1315. 8 indexed citations
16.
Song, Guojie, Qingqing Long, Yi Luo, Yiming Wang, & Yilun Jin. (2020). Deep Convolutional Neural Network Based Medical Concept Normalization. IEEE Transactions on Big Data. 8(5). 1195–1208. 4 indexed citations
17.
Long, Qingqing, Yilun Jin, Guojie Song, Yi Li, & Wei Lin. (2020). Graph Structural-topic Neural Network. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1065–1073. 45 indexed citations
18.
Long, Qingqing, Yiming Wang, Lun Du, et al.. (2019). Hierarchical Community Structure Preserving Network Embedding. 409–418. 29 indexed citations
19.
Ma, Wei, Qingqing Long, Yue Qin, Shibiao Xu, & Xiaopeng Zhang. (2018). Repairing High-Definition Ancient Paintings Based on Decomposition of Curves. Journal of Computer-Aided Design & Computer Graphics. 30(9). 1652–1652. 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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