Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Abstract We present the Temporal Graph Benchmark (TGB), a collection of challenging and
diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine …

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 …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

Large knowledge graphs often grow to store temporal facts that model the dynamic relations
or interactions of entities along the timeline. Since such temporal knowledge graphs often …

Exploiting deep learning techniques for traffic flow prediction has become increasingly
widespread. Most existing studies combine CNN or GCN with recurrent neural network to …

The proliferation of fake news on social media has the probability to bring an unfavorable
impact on public opinion and social development. Many efforts have been paid to develop …