Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, the …

In recent years, Dynamic Graph (DG) representations have been increasingly used for
modeling dynamic systems due to their ability to integrate both topological and temporal …

The rise of renewable energy and distributed generation requires new approaches to
overcome the limitations of traditional methods. In this context, Graph Neural Networks are …

Recently, there has been a trend of designing neural data structures to go beyond
handcrafted data structures by leveraging patterns of data distributions for better accuracy …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph
representation learning scenarios. However, when applied to graph data in real world …

Knowledge Graphs (KGs) are potent frameworks for knowledge representation and
reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted …

Abstract Machine learning has been increasingly used to solve management problems of
water distribution networks (WDNs). A critical research gap, however, remains in the …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …

Due to the neighborhood explosion phenomenon, scaling up graph neural networks to large
graphs remains a huge challenge. Various sampling-based mini-batch approaches, such as …

This study critically reviews the scientific literature regarding machine-learning approaches
for optimizing smart bin collection in urban environments. Usually, the problem is modeled …