"Interactive graph stream analytics in arkouda" by Zhihui Du, Oliver Alvarado Rodriguez et al.
 

Interactive graph stream analytics in arkouda

Document Type

Article

Publication Date

8-1-2021

Abstract

Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm-triangle counting-is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops.

Identifier

85111456103 (Scopus)

Publication Title

Algorithms

External Full Text Location

https://doi.org/10.3390/a14080221

e-ISSN

19994893

Issue

8

Volume

14

Grant

CCF-2109988

Fund Ref

National Science Foundation

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