112 citations · 435 across the 28 of their papers we have counts for
28 papers
Transfer learning in Scalable Graph Neural Network for Improved Physical Simulation
Siqi Shen, Yu Liu, Daniel Biggs +5
In recent years, Graph Neural Network (GNN) based models have shown promising results in simulating physics of complex systems. However, training dedicated graph network based phys…
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
Xinyi Gao, Wentao Zhang, Junliang Yu +4
Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse array of applications. However, the sheer size of large-scale graphs presents a significant challenge…
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Ling Yang, Ye Tian, Minkai Xu +7
GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output re…
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
Ling Yang, Jiayi Zheng, Heyuan Wang +5
Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are ir…
Efficient Graph Neural Network Inference at Large Scale
Xinyi Gao, Wentao Zhang, Yingxia Shao +3
Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications un…
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Ling Yang, Zhilin Huang, Yang Song +6
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and the…
Distributed Graph Neural Network Training: A Survey
Yingxia Shao, Hongzheng Li, Xizhi Gu +6
Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs,…
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang +4
Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. Howeve…
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Wentao Zhang, Zheyu Lin, Yu Shen +3
Graph neural networks (GNNs) have been intensively applied to various graph-based applications. Despite their success, manually designing the well-behaved GNNs requires immense hum…
Model Degradation Hinders Deep Graph Neural Networks
Wentao Zhang, Zeang Sheng, Ziqi Yin +5
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 l…
Graph Attention Multi-Layer Perceptron
Wentao Zhang, Ziqi Yin, Zeang Sheng +6
Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications un…
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
Yang Li, Yu Shen, Huaijun Jiang +4
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important. Motivated by the tuning behaviors of huma…
Transfer Learning based Search Space Design for Hyperparameter Tuning
Yang Li, Yu Shen, Huaijun Jiang +4
The tuning of hyperparameters becomes increasingly important as machine learning (ML) models have been extensively applied in data mining applications. Among various approaches, Ba…
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…
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Yang Li, Yu Shen, Huaijun Jiang +5
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the sc…
K-Core Decomposition on Super Large Graphs with Limited Resources
Shicheng Gao, Jie Xu, Xiaosen Li +5
K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the sc…
Efficient Diversity-Driven Ensemble for Deep Neural Networks
Wentao Zhang, Jiawei Jiang, Yingxia Shao +1
The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost…
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…
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Yu Shen, Yang Li, Jian Zheng +6
Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS met…
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…
Graph Attention MLP with Reliable Label Utilization
Wentao Zhang, Ziqi Yin, Zeang Sheng +5
Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications. Despite the high expressive power, they typically need to perform…