As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …

Graph neural networks (GNNs) learn the representation of graph-structured data, and their
expressiveness can be further enhanced by inferring node relations for propagation …

Recently, many carefully designed graph representation learning methods have achieved
impressive performance on either strong heterophilic or homophilic graphs, but not both …

Proposing an effective and flexible matrix to represent a graph is a fundamental challenge
that has been explored from multiple perspectives, eg, filtering in Graph Fourier Transforms …

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed
significant success in recommender systems (RS), capitalizing on their ability to capture …

At present, the explosive growth of software code volume and quantity makes the code
review process very labor-intensive and time-consuming. An automated code review model …

This paper proposes an novel knowledge-driven approach for resource allocation in device-
to-device (D2D) networks using a graph neural network (GNN) architecture. To meet the …

Background and objective: Electrocardiogram (ECG) analysis is crucial in diagnosing
cardiovascular diseases (CVDs). It is important to consider both temporal and spatial …

Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to
their lower-dimensional subspace, diminishing their distinguishability. However, the causes …