Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

Social network analysis is an important problem in data mining. A fundamental step for
analyzing social networks is to encode network data into low-dimensional representations …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graph representation learning has attracted increasing research attention. However, most
existing studies fuse all structural features and node attributes to provide an overarching …

Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As
the graph characteristics vary significantly in real-world systems, given a specific scenario …

Graphs with complete node attributes have been widely explored recently. While in practice,
there is a graph where attributes of only partial nodes could be available and those of the …

We explore the generalization of scattering transforms from traditional (eg, image or audio)
signals to graph data, analogous to the generalization of ConvNets in geometric deep …

The success of deep convolutional neural networks in the domains of computer vision and
speech recognition has led researchers to investigate generalizations of the said …

Random walks are widely adopted in various network analysis tasks ranging from network
embedding to label propagation. It could capture and convert geometric structures into …

Structural roles define sets of structurally similar nodes that are more similar to nodes inside
the set than outside, whereas communities define sets of nodes with more connections …