Graph Neural Networks

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to …

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only …

Neighborhood aggregation algorithms like spectral graph convolutional networks (GCNs) formulate graph convolutions as a symmetric …

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in many graph data analysis tasks. However, they still suffer …

Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to …

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph …

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from …

Heterogeneous graphs with different types of nodes and edges are ubiquitous and have immense value in many applications. Existing works …

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on …

Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. …