Interactive graph stream analytics in arkouda

Zhihui Du, Oliver Alvarado Rodriguez, Joseph Patchett, David A. Bader

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Article number221
Issue number8
StatePublished - Aug 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Numerical Analysis
  • Computational Theory and Mathematics
  • Computational Mathematics


  • Arkouda framework
  • Graph stream sketch
  • Streaming graph data
  • Triangle counting


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