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.
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
2020435 citationsTingyang Xu, Peilin Zhao et al.profile →
Graph Representation Learning via Graphical Mutual Information Maximization
2020339 citationsWenbing Huang, Yu Rong et al.profile →
Graph Convolutional Networks for Temporal Action Localization
2019335 citationsRunhao Zeng, Wenbing Huang et al.profile →
Progressive Feature Alignment for Unsupervised Domain Adaptation
2019299 citationsWenbing Huang, Yu Rong et al.profile →
Multimodal Token Fusion for Vision Transformers
2022135 citationsYikai Wang, Xinghao Chen et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Wenbing Huang'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 Wenbing Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wenbing Huang more than expected).
This network shows the impact of papers produced by Wenbing Huang. 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 Wenbing Huang. The network helps show where Wenbing Huang may publish in the future.
Co-authorship network of co-authors of Wenbing Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Wenbing Huang.
A scholar is included among the top collaborators of Wenbing Huang 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 Wenbing Huang. Wenbing Huang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rong, Yu, Wenbing Huang, Tingyang Xu, & Junzhou Huang. (2019). The Truly Deep Graph Convolutional Networks for Node Classification. arXiv (Cornell University).12 indexed citations
19.
Huang, Wenbing, Lele Cao, Fuchun Sun, et al.. (2016). Learning stable linear dynamical systems with the weighted least square method. International Joint Conference on Artificial Intelligence. 1599–1605.7 indexed citations
20.
Huang, Wenbing, Deli Zhao, Fuchun Sun, Huaping Liu, & Edward Yi Chang. (2015). Scalable Gaussian process regression using deep neural networks. International Conference on Artificial Intelligence. 3576–3582.26 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.