Abstract Graph Neural Networks (GNNs) have been shown to be effective models for
different predictive tasks on graph-structured data. Recent work on their expressive power …

Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

The quantum approximate optimization algorithm (QAOA) is one of the most promising
candidates for achieving quantum advantage through quantum-enhanced combinatorial …

Graph pooling is a central component of a myriad of graph neural network (GNN)
architectures. As an inheritance from traditional CNNs, most approaches formulate graph …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

As large-scale graphs become increasingly more prevalent, it poses significant
computational challenges to process, extract and analyze large graph data. Graph …

Community detection is an important task with many applications. However, there is no
universal definition of communities, and a variety of algorithms have been proposed based …