Arachne: An Arkouda Package for Large-Scale Graph Analytics

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data scientists to handle such graphs efficiently. These graphs may include social networks, computer networks, and genomes. In this paper, we propose a novel graph package Arachne to make large-scale graph analytics more effortless and more efficient based on the open-source Arkouda framework, which has been developed to allow users to perform massively parallel computations on distributed data with an interface similar to NumPy. In this package, we developed a fundamental sparse graph data structure and several useful graph algorithms around our data structure to build a basic algorithmic library. Benchmarks and tools have also been developed to evaluate and demonstrate the provided graph algorithms. The graph algorithms we have implemented thus far include breadth-first search (BFS), connected components (CC), k-Truss (KT), Jaccard coefficients (JC), triangle counting (TC), and triangle centrality (TCE). Their corresponding experimental results based on realworld and synthetic graphs are presented.

Identifier

85145019373 (Scopus)

ISBN

[9781665497862]

Publication Title

2022 IEEE High Performance Extreme Computing Conference Hpec 2022

External Full Text Location

https://doi.org/10.1109/HPEC55821.2022.9991947

Volume

2022-January

Grant

CCF-2109988

Fund Ref

National Science Foundation

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