Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in
learning distributed representations from graph-structured data. However, centralizing a …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

The digital deep integration of cyber space, physical space and social space facilitates the
formation of Cyber–Physical–Social Systems (CPSS). Knowledge empowers CPSS to be …

Graphs are widely used to model interconnected entities and improve downstream
predictions in various real-world applications. However, real-world graphs nowadays are …

Graph neural networks (GNNs) have achieved superior performance in various applications,
but training dedicated GNNs can be costly for large-scale graphs. Some recent work started …

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 …

Cold-start recommendation is a challenging problem due to the lack of user-item
interactions. Recently, heterogeneous information network~(HIN)-based recommendation …

A heterogeneous information network (HIN) has as vertices objects of different types and as
edges the relations between objects, which are also of various types. We study the problem …

Graph convolutional networks (GCNs) are a powerful class of graph neural networks.
Trained in a semi-supervised end-to-end fashion, GCNs can learn to integrate node features …

Federated learning (FL) aims to train powerful and generalized global models without
putting distributed data together, which has been shown effective in various domains of …