Zengfeng Huang

1.5k citations
44 papers · 726 indexed · 1 hit paper · h-index 12
Topics
Advanced Graph Neural Networks (15 papers)Complexity and Algorithms in Graphs (8 papers)Complex Network Analysis Techniques (7 papers)

In The Last Decade

Zengfeng Huang

41 papers receiving 706 citations

Hit Papers

Simple and Deep Graph Convolutional Networks2020202620222024202050100150200

Peers

Zengfeng Huang
Comparison fields: 5 of 73
  • Artificial Intelligence 487
  • Computer Networks and Communications 246
  • Computer Vision and Pattern Recognition 158
  • Signal Processing 153
  • Information Systems 128
Replace Fenlin Liu with:
Fenlin Liu China
Sayan Ranu India
Makoto Onizuka Japan
Xiongjie Zhu China
Nisheeth Shrivastava United States
Kai Zhao China
Miles E. Smid United States
E. Koutsofios United States
Weiyi Liu China
Siqiang Luo Singapore
Zengfeng Huang relative to Fenlin Liu China Fenlin Liu's profile →
Citations per field
00.5×1.5×
Fenlin Liu · 1×
Citations per year

Countries citing papers authored by Zengfeng Huang

Since Specialization
Citations

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

Fields of papers citing papers by Zengfeng Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zengfeng Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Zengfeng Huang. A scholar is included among the top collaborators of Zengfeng 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 Zengfeng Huang. Zengfeng Huang 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
#WorkIndexed citations
1 0
2 2
3 0
4 1
5 4
6 2
7 15
8 11
9 5
10 3
11
Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA
2
12 25
13 38
14
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
6
15
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
4
16 1
17 1
18 78
19 27
20 15

About Zengfeng Huang

Zengfeng Huang is a scholar working on Acoustics and Ultrasonics, Artificial Intelligence and Computer Networks and Communications, having authored 44 papers that have together received 726 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (15 papers), Complexity and Algorithms in Graphs (8 papers) and Complex Network Analysis Techniques (7 papers). The work is most often cited by research in Artificial Intelligence (487 citations), Acoustics and Ultrasonics (13 citations) and Signal Processing (153 citations). Zengfeng Huang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Zhewei Wei, Ke Yi, Yaliang Li, Bolin Ding, Ming Chen, Jeff M. Phillips, Pankaj K. Agarwal, Graham Cormode, Wenjie Zhang and Xuemin Lin. Their work appears in journals such as PLoS ONE, IEEE Transactions on Information Theory and Optics Express.

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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