Paper Info
Title
An incremental GraphBLAS solution for the 2018 TTC Social Media case study
Abstract
Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the “Social Media” case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts.
Year
DOI
Venue
2020
10.1109/IPDPSW50202.2020.00045
2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Keywords
DocType
ISSN
linear algebraic graph computational model,incremental GraphBLAS solution,2018 TTC Social Media case study,graph databases,graph processing,API,data set analysis,transactional read operations
Conference
2164-7062
ISBN
Citations 
PageRank 
978-1-7281-7457-0
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Márton Elekes100.68
Gábor Szárnyas2537.84