Provable and practical approximations for the degree distribution using
sublinear graph samples

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Provable and practical approximations for the degree distribution using sublinear graph samples

24 October 2017

Seshadhri Comandur

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Papers citing "Provable and practical approximations for the degree distribution using sublinear graph samples"

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Title
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting Hao Wu Haomin Wen Guibin Zhang Yutong Xia Kai Wang Yuxuan Liang Yu Zheng Kun Wang 195 2 0 17 Jan 2025
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks Gao Li M. Duda Xinming Zhang Danai Koutra Yujun Yan 91 9 0 26 Jun 2023
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks Shuai Zhang Ming Wang Pin-Yu Chen Sijia Liu Songtao Lu Miaoyuan Liu MLT 125 17 0 06 Feb 2023
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning Chunhui Zhang Chao Huang Yijun Tian Qianlong Wen Z. Ouyang Youhuan Li Yanfang Ye Chuxu Zhang 69 8 0 01 Oct 2022
Learning to Drop: Robust Graph Neural Network via Topological Denoising Dongsheng Luo Wei Cheng Wenchao Yu Bo Zong Jingchao Ni Haifeng Chen Xiang Zhang OOD 100 266 0 13 Nov 2020
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification R. S. Srinivasa Cao Xiao Lucas Glass Justin Romberg Jimeng Sun GNN 71 28 0 15 Jun 2020
Inferring Degrees from Incomplete Networks and Nonlinear Dynamics Thanh Tien Vu Dat Quoc Nguyen Anthony N. Nguyen 95 75 0 21 Apr 2020
Community Detection in Partially Observable Social Networks Cong Tran Won-Yong Shin Andreas Spitz 68 34 0 30 Dec 2017