With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

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

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …

Graph neural networks (GNNs) have been widely used in representation learning on graphs
and achieved state-of-the-art performance in tasks such as node classification and link …

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

Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …

Abstract Convolutional Neural Networks (CNNs) achieve impressive performance in a wide
variety of fields. Their success benefited from a massive boost when very deep CNN models …

Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing,
but their wide applications are hampered by a lack of knowledge governing guide RNA …

Abstract We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework
for deep learning on graphs. It is based on discretizations of a second-order system of …