56 citations · 233 across the 11 of their papers we have counts for
11 papers
ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest
Yuezihan Jiang, Yu Cheng, Hanyu Zhao +6
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. ZOOMER is desi…
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Wentao Zhang, Yexin Wang, Zhenbang You +5
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires consider…
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Wentao Zhang, Yu Shen, Zheyu Lin +6
Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing m…
RIM: Reliable Influence-based Active Learning on Graphs
Wentao Zhang, Yexin Wang, Zhenbang You +5
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to…
Node Dependent Local Smoothing for Scalable Graph Learning
Wentao Zhang, Mingyu Yang, Zeang Sheng +5
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs). Concretely, they show feature smoothing combined with simple linear regression…
Evaluating Deep Graph Neural Networks
Wentao Zhang, Zeang Sheng, Yuezihan Jiang +4
Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment t…