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

Travel-time prediction constitutes a task of high importance in transportation networks, with
web map** services like Google Maps regularly serving vast quantities of travel time …

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 …

Combinatorial optimization problems are pervasive across science and industry. Modern
deep learning tools are poised to solve these problems at unprecedented scales, but a …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …

Recent advancements in Natural Language Processing (NLP) have led to the development
of NLP-based recommender systems that have shown superior performance. However …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …