Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …

We investigate graph neural networks on graphs with heterophily. Some existing methods
amplify a node's neighborhood with multi-hop neighbors to include more nodes with …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Molecular property prediction (MPP) is a crucial and fundamental task for AI-aided drug
discovery (AIDD). Recent studies have shown great promise of applying self-supervised …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

Real-world data generation often involves complex inter-dependencies among instances,
violating the IID-data hypothesis of standard learning paradigms and posing a challenge for …

Abstract Graph Neural Networks (GNNs) have received extensive research attention for their
promising performance in graph machine learning. Despite their extraordinary predictive …

Due to the over-smoothing issue, most existing graph neural networks can only capture
limited dependencies with their inherently finite aggregation layers. To overcome this …

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide
a promising way to model higher-order relations in data and further solve relevant prediction …

Over the past few years, graph neural networks (GNN) and label propagation-based
methods have made significant progress in addressing node classification tasks on graphs …