High-quality datasets can speed up breakthroughs and reveal potential
de...
Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous
L...
Entity alignment aims to discover unique equivalent entity pairs with th...
Knowledge graphs (KGs) are known for their large scale and knowledge
inf...
Deep Neural Networks (DNNs) have recently achieved great success in many...
Link prediction aims to infer the existence of a link between two nodes ...
Graph Neural Networks (GNNs) have achieved state-of-the-art results for
...
We introduce M2DGR: a novel large-scale dataset collected by a ground ro...
Graph neural networks have emerged as a powerful model for graph
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While artificial intelligence (AI) is widely applied in various areas, i...
Graph neural networks (GNNs) have achieved state-of-the-art performance ...
Graph convolutional networks (GCNs) and their variants have achieved gre...
Botnet is one of the major threats to computer security. In previous bot...
Active learning (AL) on attributed graphs has received increasing attent...
Deep Neural Networks (DNNs) have recently achieved great success in many...
Traditional network embedding primarily focuses on learning a dense vect...
Network embedding aims to learn a latent, low-dimensional vector
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Bilayer bending based soft actuators are widely utilized in soft robotic...
Climbing soft robots are of tremendous interest in both science and
engi...
Despite outperforming the human in many tasks, deep neural network model...
Two types of framework for blurred image classification based on adaptiv...