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

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

Researchers

Research units

  • VTT Technical Research Centre of Finland
  • Ca’ Foscari University of Venice

Abstract

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.

Details

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
Publication statusPublished - 22 Jan 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - Seattle, United States
Duration: 10 Dec 201813 Dec 2018
http://cci.drexel.edu/bigdata/bigdata2018/index.html

Conference

ConferenceIEEE International Conference on Big Data
Abbreviated titleBig Data
CountryUnited States
CitySeattle
Period10/12/201813/12/2018
Internet address

ID: 31473578