Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering
(CF) approaches for recommender systems. The key idea of GNN-based recommender …

Abstract Generative Adversarial Networks (GANs) are a type of deep learning architecture
that uses two networks namely a generator and a discriminator that, by competing against …

Contrastive learning has recently emerged as a powerful technique for graph self-
supervised pretraining (GSP). By maximizing the mutual information (MI) between a positive …

The prevalence of multi_drug therapies has been increasing in recent years, particularly
among the elderly who are suffering from several diseases. However, unexpected …

Anomaly analytics is a popular and vital task in various research contexts that has been
studied for several decades. At the same time, deep learning has shown its capacity in …

Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

Generating graph-structured data is a challenging problem, which requires learning the
underlying distribution of graphs. Various models such as graph VAE, graph GANs, and …

Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …