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-level representations are critical in various real-world applications, such as predicting
the properties of molecules. However, in practice, precise graph annotations are generally …

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

The representation of heterogeneous graph nodes has become a hot research topic due to
its diverse applications. However, extant approaches can only give consideration partly to …

In this study, we propose a graph sequence neural network (GSNN) to accurately decode
patterns of motor imagery from electroencephalograms (EEGs) in the presence of …

Graph neural networks (GNNs) have advanced graph classification tasks, where a global
pooling to generate graph representations by summarizing node features plays a critical role …

Fake news can be easily propagated through social media and cause negative societal
effects. Since fake news is disinformation with malicious intent, manual fact-checking …

In this study, we proposed a novel method called the graph capsule convolutional network
(GCCN) to predict the progression from mild cognitive impairment to dementia and identify …

Graph pooling refers to the operation that maps a set of node representations into a compact
form for graph-level representation learning. However, existing graph pooling methods are …

Subgraph federated learning (SFL) is a research methodology that has gained significant
attention for its potential to handle distributed graph-structured data. In SFL, the local model …