Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …

Homophily is a graph property describing the tendency of edges to connect similar nodes;
the opposite is called heterophily. It is often believed that heterophilous graphs are …

Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it
is susceptible to low-quality and unreliable structure, which has been a norm rather than an …

Out-of-distribution (OOD) generalization has gained increasing attentions for learning on
graphs, as graph neural networks (GNNs) often exhibit performance degradation with …

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised
learning approach for graph-structured data. Despite its remarkable success, existing GCL …

It has been proven that graph neural networks (GNNs) are effective for a variety of graph
learning-based applications. Typical GNNs iteratively aggregate messages from immediate …

Influence maximization (IM) aims to identify a small number of influential individuals to
maximize the information spread and finds applications in various fields. It was first …

Abstract Among the Graph Neural Network (GNN) models that address the task of node
classification, Graph Echo State Networks (GESN) have proved particularly effective in …

Despite graph neural networks'(GNNs) great success in modelling graph-structured data,
out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of …

Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most
existing CL methods deal with independent data (eg, images and text) for which many …