Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …

Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining,
leading to significant advances across various domains. Stemmed from the node-wise …

Abstract Graph Lottery Ticket (GLT), a combination of core subgraph and sparse
subnetwork, has been proposed to mitigate the computational cost of deep Graph Neural …

Graph Neural Networks~(GNNs) have emerged as a powerful category of learning
architecture for handling graph-structured data. However, existing GNNs typically ignore …

A significant amount of society's infrastructure can be modeled using graph structures, from
electric and communication grids, to traffic networks, to social networks. Each of these …

Graph neural networks (GNNs), and especially message-passing neural networks, excel in
various domains such as physics, drug discovery, and molecular modeling. The expressivity …

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has
demonstrated significant advantages in semi-supervised node prediction tasks with …

Hierarchical graph pooling effectively captures hierarchical structural information by
iteratively simplifying the input graph into smaller graphs using a pooling function, which has …

In graph-level representation learning tasks, graph neural networks have received much
attention for their powerful feature learning capabilities. However, with the increasing scales …