Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

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 …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when
performing supervised deep learning, a WSI is divided into small patches, trained and the …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …