59 citations · 180 across the 28 of their papers we have counts for
7 papers · 1 filter
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…
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…
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
Chen Zhu, Liang Du, Hong Chen +4
Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditio…
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation
Hong Chen, Yipeng Zhang, Simin Wu +4
Subject-driven text-to-image generation aims to generate customized images of the given subject based on the text descriptions, which has drawn increasing attention. Existing metho…
Curriculum Graph Machine Learning: A Survey
Haoyang Li, Xin Wang, Wenwu Zhu
Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct t…