Graph data in the real world is often accompanied by the problem of missing attributes.
Recently, self-supervised graph representation learning, implementing data imputation …

Exploring simple and efficient computational methods for drug repositioning has emerged as
a popular and compelling topic in the realm of comprehensive drug development. The crux …

Abstract In recent years, Graph Neural Networks (GNNs), an emerging technology for
learning from graph-structured data, have attracted much attention. Despite the widespread …

Unsupervised region representation learning aims to extract dense and effective features
from unlabeled urban data. While some efforts have been made for solving this problem …

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …

Graph convolutional networks (GCNs) can quickly and accurately learn graph
representations and have shown powerful performance in many graph learning domains …

Unsupervised multi-view representation learning has been studied extensively for mining
multi-view data. However, some critical challenges remain. On the one hand, the existing …

As the accumulation of multi-view data continues to grow, multi-view clustering has become
increasingly important in research fields like data mining. However, current methods have …

Network representation learning is an important tool for extracting latent features from
heterogeneous networks to enhance downstream analysis tasks. However, for …

Most existing heterogeneous graph neural network models need more effective integration
and full exploitation of features at different network levels to prevent overfitting. To address …