Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Link prediction in complex networks has attracted considerable attention from
interdisciplinary research communities, due to its ubiquitous applications in biological …

Graph embedding based on random-walks supports effective solutions for many graph-
related downstream tasks. However, the abundance of embedding literature has made it …

It is tough to detect unexpected drug–drug interactions (DDIs) in poly-drug treatments
because of high costs and clinical limitations. Computational approaches, such as deep …

Graph structural information such as topologies or connectivities provides valuable
guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing …

The goal of directed network embedding is to represent the nodes in a given directed
network as embeddings that preserve the asymmetric relationships between nodes. While a …

Node representation learning for directed graphs is critically important to facilitate many
graph mining tasks. To capture the directed edges between nodes, existing methods mostly …

There has been significant progress in unsupervised network representation learning
(UNRL) approaches over graphs recently with flexible random-walk approaches, new …

Graph structures are ubiquitous throughout the natural sciences. Here we develop an
approach that exploits the quantum source's graph structure to improve learning via an …

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node
classification tasks on graphs. While these improvements have been largely demonstrated …