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 …

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

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 …

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 …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

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 …

Graph neural networks (GNNs) have become the defacto standard for representation
learning on graphs, which derive effective node representations by recursively aggregating …

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 …

We propose a unified framework for adaptive connection sampling in graph neural networks
(GNNs) that generalizes existing stochastic regularization methods for training GNNs. The …

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 …