Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph neural networks (GNNs) have recently become the standard approach for learning
with graph-structured data. Prior work has shed light into their potential, but also their …

Graph neural networks (GNNs) have shown promising results across various graph learning
tasks, but they often assume homophily, which can result in poor performance on …

Recently, the paradigm of pre-training and fine-tuning graph neural networks has been
intensively studied and applied in a wide range of graph mining tasks. Its success is …

Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …

Heterogeneous graph neural networks (HGNNs) can effectively model multiple node types
and complex interactions in real networks and solve problems in various practical …

The message-passing paradigm has served as the foundation of graph neural networks
(GNNs) for years, making them achieve great success in a wide range of applications …

Over the past decade, Graph Neural Networks (GNNs) have achieved great success on
machine learning tasks with relational data. However, recent studies have found that …

In recent years, the cryptocurrency market has been booming with an ever-increasing
market capitalization. However, due to the anonymity of blockchain technology, this market …

The paradigm of pre-training and fine-tuning graph neural networks has attracted wide
research attention. In previous studies, the pre-trained models are viewed as universally …