12 citations · 51 across the 18 of their papers we have counts for
14 papers · 1 filter
Towards Multimodal Graph Large Language Model
Xin Wang, Zeyang Zhang, Linxin Xiao +3
Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are…
Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
Xin Wang, Haoyang Li, Haibo Chen +2
Large language models (LLMs) have substantially advanced machine learning research, including natural language processing, computer vision, data mining, etc., yet they still exhibi…
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
Beini Xie, Heng Chang, Ziwei Zhang +5
Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in res…
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…
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision
Zeyang Zhang, Xin Wang, Ziwei Zhang +3
The existing graph neural architecture search (GNAS) methods heavily rely on supervised labels during the search process, failing to handle ubiquitous scenarios where supervisions…
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
Zeyang Zhang, Xin Wang, Ziwei Zhang +5
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution se…
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
Zeyang Zhang, Xingwang Li, Fei Teng +4
Human albumin is essential for indicating the body's overall health. Accurately predicting plasma albumin levels and determining appropriate doses are urgent clinical challenges, p…
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
Zeyang Zhang, Xin Wang, Ziwei Zhang +2
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to ha…
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…
Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum
Zeyang Zhang, Ziwei Zhang, Xin Wang +1
Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods ad…
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…