Constraining cosmology with big data statistics of cosmological graphs

Sungryong Hong, Donghui Jeong, Ho Seong Hwang, Juhan Kim, Sungwook E. Hong, Changbom Park, Arjun Dey, Milos Milosavljevic, Karl Gebhardt, Kyoung Soo Lee

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


By utilizing large-scale graph analytic tools implemented in the modern big data platform, APACHE SPARK, we investigate the topological structure of gravitational clustering in five different universes produced by cosmological N-body simulations with varying parameters: (1) a WMAP 5-yr compatible ∆CDM cosmology, (2) two different dark energy equation of state variants, and (3) two different cosmic matter density variants. For the big data calculations, we use a custom build of standalone Spark/Hadoop cluster at Korea Institute for Advanced Study and Dataproc Compute Engine in Google Cloud Platform with sample sizes ranging from 7 to 200 million. We find that among the many possible graph-topological measures, three simple ones: (1) the average of number of neighbours (the so-called average vertex degree) α, (2) closed-to-connected triple fraction (the so-called transitivity) τ∆, and (3) the cumulative number density ns5 of subgraphs with connected component size s ≥ 5, can effectively discriminate among the five model universes. Since these graph-topological measures are directly related with the usual n-points correlation functions of the cosmic density field, graph-topological statistics powered by big data computational infrastructure opens a new, intuitive, and computationally efficient window into the dark Universe.

Original languageEnglish (US)
Pages (from-to)5972-5986
Number of pages15
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
StatePublished - Apr 1 2021

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


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