Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

Pre-routing timing prediction has been recently studied for evaluating the quality of a
candidate cell placement in chip design. It involves directly estimating the timing metrics for …

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …

Domain generalization on graphs aims to develop models with robust generalization
capabilities, ensuring effective performance on the testing set despite disparities between …

Graphs have become an important modeling tool for web applications, and Graph Neural
Networks (GNNs) have achieved great success in graph representation learning. However …

Currently, two issues exist in the field of graph Siamese representation learning. First, the
strategies for positive sample selection often impose strict constraints on the candidate set …

Message passing plays a vital role in graph neural networks (GNNs) for effective feature
learning. However, the over-reliance on input topology diminishes the efficacy of message …

Distribution shifts on graphs--the discrepancies in data distribution between training and
employing a graph machine learning model--are ubiquitous and often unavoidable in real …

Mixup has shown considerable success in mitigating the challenges posed by limited
labeled data in image classification. By synthesizing samples through the interpolation of …