12 citations · 38 across the 10 of their papers we have counts for
10 papers
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
Chendi Ge, Xin Wang, Ziwei Zhang +6
Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture co…
JAQ: Joint Efficient Architecture Design and Low-Bit Quantization with Hardware-Software Co-Exploration
Mingzi Wang, Yuan Meng, Chen Tang +9
The co-design of neural network architectures, quantization precisions, and hardware accelerators offers a promising approach to achieving an optimal balance between performance an…
Causal-aware Graph Neural Architecture Search under Distribution Shifts
Peiwen Li, Xin Wang, Zeyang Zhang +5
Graph NAS has emerged as a promising approach for autonomously designing GNN architectures by leveraging the correlations between graphs and architectures. Existing methods fail to…
Exploring the Potential of Large Language Models in Graph Generation
Yang Yao, Xin Wang, Zeyang Zhang +6
Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. Ho…
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs
Yijian Qin, Xin Wang, Ziwei Zhang +1
Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attentio…
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
Zeyang Zhang, Xin Wang, Ziwei Zhang +3
In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, partic…
Graph Meets LLMs: Towards Large Graph Models
Ziwei Zhang, Haoyang Li, Zeyang Zhang +3
Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large mode…
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
Yijian Qin, Ziwei Zhang, Xin Wang +2
Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further rese…
AutoGL: A Library for Automated Graph Learning
Ziwei Zhang, Yijian Qin, Zeyang Zhang +11
Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for…
Democratizing Production-Scale Distributed Deep Learning
Minghuang Ma, Hadi Pouransari, Daniel Chao +5
The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry. However, trainin…