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 …

With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean
network data. Recently, there emerge a number of works to investigate the robustness of …

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

Plenty of models have been presented to handle the hypergraph node classification.
However, very few of these methods consider contrastive learning, which is popular due to …

This article proposes a quantum spatial graph convolutional neural network (QSGCN) model
that is implementable on quantum circuits, providing a novel avenue to processing non …

As the noisy intermediate-scale quantum (NISQ) era has begun, a quantum neural network
(QNN) is definitely a promising solution to many problems that classical neural networks …

Heterogeneous information networks (HINs) are potent models of complex systems. In
practice, many nodes in an HIN have their attributes unspecified, resulting in significant …

With the rapid development of deep neural networks, cross-modal hashing has made great
progress. However, the information of different types of data is asymmetrical, that is to say, if …

Recommender systems filter information to meet users' personalized interests actively.
Existing graph-based models typically extract users' interests from a heterogeneous …