Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Over the past couple of decades, the explosion of densely interconnected data has
stimulated the research, development and adoption of graph database technologies. From …

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud
services, and serverless functions have immediately become a new middleware for building …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

We introduce a high-performance cost-effective network topology called Slim Fly that
approaches the theoretically optimal network diameter. Slim Fly is based on graphs that …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

The Jaccard similarity index is an important measure of the overlap of two sets, widely used
in machine learning, computational genomics, information retrieval, and many other areas …

Graph databases (GDBs) enable processing and analysis of unstructured, complex, rich,
and usually vast graph datasets. Despite the large significance of GDBs in both academia …

Link prediction is one of the central problems in graph mining. However, recent studies
highlight the importance of higher-order network analysis, where complex structures called …

Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in develo** GDBs are achieving high performance, scalability …