With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Recent works reveal that feature or label smoothing lies at the core of Graph Neural
Networks (GNNs). Concretely, they show feature smoothing combined with simple linear …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks.
However, drastic performance degradation is always observed when a GNN is stacked with …

Embedding models have been an effective learning paradigm for high-dimensional data.
However, one open issue of embedding models is that their representations (latent factors) …

The study of graph neural networks has revealed that they can unleash new applications in
a variety of disciplines using such a basic process that we cannot imagine in the context of …

Recently, graph neural networks (GNNs) have shown prominent performance in graph
representation learning by leveraging knowledge from both graph structure and node …

Embedding models have been recognized as an effective learning paradigm for high-
dimensional data. However, a major embedding model training obstacle is that updating …