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An Incremental GraphBLAS Solution for the 2018 TTC Social Media Case Study

Elekes, Márton and Szárnyas, Gábor (2020) An Incremental GraphBLAS Solution for the 2018 TTC Social Media Case Study. In: GrAPL 2020: Workshop on Graphs, Architectures, Programming, and Learning.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA166-QA166.245 Graphs theory / gráfelmélet
Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Depositing User: Márton Elekes
Date Deposited: 03 Aug 2020 13:45
Last Modified: 03 Apr 2023 06:52
URI: http://real.mtak.hu/id/eprint/111910

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