Analysis of large sparse graphs using regular decomposition of graph distance matrices

Hannu Reittu, Lasse Leskelä, Marco Fiorucci, Tomi Räty

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    8 Citations (Scopus)
    121 Downloads (Pure)


    Statistical analysis of large and sparse graphs is a challenging problem in data science due to the high dimensionality and nonlinearity of the problem. This paper presents a fast and scalable algorithm for partitioning such graphs into disjoint groups based on observed graph distances from a set of reference nodes. The resulting partition provides a low-dimensional approximation of the full distance matrix which helps to reveal global structural properties of the graph using only small samples of the distance matrix. The presented algorithm is inspired by the information-theoretic minimum description principle. We investigate the performance of this algorithm for selected real data sets and for synthetic graph data sets generated using stochastic block models and power-law random graphs, together with analytical considerations for sparse stochastic block models with bounded average degrees.
    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Big Data (Big Data)
    EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-5386-5035-6
    ISBN (Print)978-1-5386-5036-3, 978-1-5386-5034-9
    Publication statusPublished - 2 Jul 2018
    MoE publication typeA4 Article in a conference publication
    EventAdvances in High Dimensional Big Data: Workshop in conjunction with the 2018 IEEE International Conference on Big Data (IEEE Big Data 2018) - Seattle, United States
    Duration: 10 Dec 201813 Dec 2018


    WorkshopAdvances in High Dimensional Big Data
    Country/TerritoryUnited States


    • graph theory
    • statistical analysis
    • Big Data
    • peer-to-peer computing
    • partitioning
    • approximation algorithms
    • stochastic processes


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