Model Degradation Hinders Deep Graph Neural Networks

1 code implementation • 9 Jun 2022 • Wentao Zhang, Zeang Sheng, Ziqi Yin, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, Bin Cui

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers.

Attribute Graph Mining

Instance-wise Prompt Tuning for Pretrained Language Models

no code implementations • 4 Jun 2022 • Yuezihan Jiang, Hao Yang, Junyang Lin, Hanyu Zhao, An Yang, Chang Zhou, Hongxia Yang, Zhi Yang, Bin Cui

Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.

ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

1 code implementation • 20 Mar 2022 • Yuezihan Jiang, Yu Cheng, Hanyu Zhao, Wentao Zhang, Xupeng Miao, Yu He, Liang Wang, Zhi Yang, Bin Cui

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs.

Retrieval

Evaluating Deep Graph Neural Networks

1 code implementation • 2 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 implementation • 25 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.

Clustering Graph Learning +5