Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries
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 …
even trillions of edges. Often, their structure changes over time and they have domain …
Sebs: A serverless benchmark suite for function-as-a-service computing
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 …
services, and serverless functions have immediately become a new middleware for building …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
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 …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Parallel and distributed graph neural networks: An in-depth concurrency analysis
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 …
routinely solve complex problems on unstructured networks, such as node classification …
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores
Graph databases (GDBs) are crucial in academic and industry applications. The key
challenges in developing GDBs are achieving high performance, scalability …
challenges in developing GDBs are achieving high performance, scalability …
Motif prediction with graph neural networks
M Besta, R Grob, C Miglioli, N Bernold… - Proceedings of the 28th …, 2022 - dl.acm.org
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 …
highlight the importance of higher-order network analysis, where complex structures called …
Neural graph databases
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 …
and usually vast graph datasets. Despite the large significance of GDBs in both academia …
Graphminesuite: Enabling high-performance and programmable graph mining algorithms with set algebra
M Besta, Z Vonarburg-Shmaria, Y Schaffner… - arXiv preprint arXiv …, 2021 - arxiv.org
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that
facilitates evaluating and constructing high-performance graph mining algorithms. First …
facilitates evaluating and constructing high-performance graph mining algorithms. First …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …