Link prediction aims to anticipate the probability of a future connection between two nodes in
a given network based on their previous interactions and the network structure. Link …

Exploring new therapeutic diseases for existing drugs plays an essential role in reducing
drug development costs. However, existing methods for predicting drug–disease association …

Graph convolutional network (GCN) is one of the most representative methods in the realm
of graph neural networks (GNNs). In the convolution process, GCN combines the structural …

As a fundamental problem in network science, link prediction is both theoretically significant
and practically useful. Many existing link prediction algorithms rely on predefined …

Dental caries is one of the most prevalent oral diseases, and deep learning methods have
been used for caries diagnosis in large populations by leveraging RGB images. The existing …

Abstract Information networks generally exhibit three characteristics, namely dynamicity,
heterogeneity, and node attribute diversity. However, most existing network embedding …

Exploiting heterogeneous information in attributed networks to improve the performance of
community detection has attracted considerable research attention. Although variational …

Graph convolutional network (GCN) has gained considerable attention and has been widely
utilized in graph data analytics. However, training large GCNs presents considerable …

In large-scale power systems, accurately detecting and diagnosing the type of faults when
they occur in the grid is a challenging problem. The classification performance of most …

Heterogeneous temporal network embedding aims to learn each node of different types of a
heterogeneous temporal network in each snapshot into a low-dimensional vector …