Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention
in recent years and achieved outstanding performance in many tasks. The success of the …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Node classification tasks aim to assign labels or categories to entire graphs based on their
structural properties or node attributes. It can be adopted for various types of graph systems …

Abstract Graph Neural Networks (GNNs) have achieved excellent performance of graph
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …

Multiple time series forecasting plays an essential role in many applications. Solutions
based on graph neural network (GNN) that deliver state-of-the-art forecasting performance …

Abstract Graph Neural Networks (GNNs) have received considerable attention in recent
years due to their unique ability to model both topologies and semantics in the graphs. In …

At online retail platforms, it is crucial to actively detect the risks of transactions to improve
customer experience and minimize financial loss. In this work, we propose xFraud, an …

Graph mining tasks often suffer from the lack of supervision from labeled information due to
the intrinsic sparseness of graphs and the high cost of manual annotation. To alleviate this …