Search Results for author: Zeang Sheng

Found 5 papers, 3 papers with code

NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning

no code implementations29 Sep 2021 Wentao Zhang, Zeang Sheng, Mingyu Yang, Yang Li, Yu Shen, Zhi Yang, Zichao Yang, Bin Cui

First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.

Graph Representation Learning Link Prediction +1

Graph Attention Multi-Layer Perceptron

2 code implementations23 Aug 2021 Wentao Zhang, Ziqi Yin, Zeang Sheng, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, Bin Cui

Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications.

Graph Attention

Evaluating Deep Graph Neural Networks

no code implementations2 Aug 2021 Wentao Zhang, Zeang Sheng, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, Bin Cui

Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs; (2) when we need and how to build deep GNNs.

Graph Mining Node Classification

ROD: Reception-aware Online Distillation for Sparse Graphs

1 code implementation25 Jul 2021 Wentao Zhang, Yuezihan Jiang, Yang Li, Zeang Sheng, Yu Shen, Xupeng Miao, Liang Wang, Zhi Yang, Bin Cui

Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.

Graph Learning Knowledge Distillation +4

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