Self-supervised learning of graph neural networks: A unified review
Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …
Graph prompt learning: A comprehensive survey and beyond
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …
Drum: End-to-end differentiable rule mining on knowledge graphs
A Sadeghian, M Armandpour… - Advances in Neural …, 2019 - proceedings.neurips.cc
In this paper, we study the problem of learning probabilistic logical rules for inductive and
interpretable link prediction. Despite the importance of inductive link prediction, most …
interpretable link prediction. Despite the importance of inductive link prediction, most …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
How to build a graph-based deep learning architecture in traffic domain: A survey
In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …
Automated self-supervised learning for recommendation
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …
collaborative filtering (CF). To improve the representation quality over limited labeled data …
Learning to pre-train graph neural networks
Graph neural networks (GNNs) have become the defacto standard for representation
learning on graphs, which derive effective node representations by recursively aggregating …
learning on graphs, which derive effective node representations by recursively aggregating …
Variational graph neural networks for road traffic prediction in intelligent transportation systems
As one of the most important applications of industrial Internet of Things, intelligent
transportation system aims to improve the efficiency and safety of transportation networks. In …
transportation system aims to improve the efficiency and safety of transportation networks. In …
Bayesian graph neural networks with adaptive connection sampling
A Hasanzadeh, E Hajiramezanali… - International …, 2020 - proceedings.mlr.press
We propose a unified framework for adaptive connection sampling in graph neural networks
(GNNs) that generalizes existing stochastic regularization methods for training GNNs. The …
(GNNs) that generalizes existing stochastic regularization methods for training GNNs. The …
Graph clustering via variational graph embedding
L Guo, Q Dai - Pattern Recognition, 2022 - Elsevier
Graph clustering based on embedding aims to divide nodes with higher similarity into
several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph …
several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph …