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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Reittu, Hannu
dc.contributor.author Leskelä, Lasse
dc.contributor.author Räty, Tomi
dc.contributor.author Fiorucci, Marco
dc.contributor.editor Song, Yang
dc.contributor.editor Liu, Bing
dc.contributor.editor Lee, Kisung
dc.contributor.editor Abe, Naoki
dc.contributor.editor Pu, Calton
dc.contributor.editor Qiao, Mu
dc.contributor.editor Ahmed, Nesreen
dc.contributor.editor Kossmann, Donald
dc.contributor.editor Saltz, Jeffrey
dc.contributor.editor Tang, Jiliang
dc.contributor.editor He, Jingrui
dc.contributor.editor Liu, Huan
dc.contributor.editor Hu, Xiaohua
dc.date.accessioned 2019-02-25T08:50:35Z
dc.date.available 2019-02-25T08:50:35Z
dc.date.issued 2019-01-22
dc.identifier.citation Reittu , H , Leskelä , L , Räty , T & Fiorucci , M 2019 , Analysis of large sparse graphs using regular decomposition of graph distance matrices . in 2018 IEEE International Conference on Big Data (Big Data) . IEEE , pp. 3784-3792 , IEEE International Conference on Big Data , Seattle , United States , 10/12/2018 . https://doi.org/10.1109/BigData.2018.8622118 en
dc.identifier.isbn 978-1-5386-5036-3
dc.identifier.isbn 978-1-5386-5035-6
dc.identifier.other PURE UUID: 9dee2047-1471-43d7-bdb8-e21bea1c703e
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/analysis-of-large-sparse-graphs-using-regular-decomposition-of-graph-distance-matrices(9dee2047-1471-43d7-bdb8-e21bea1c703e).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85062642513&partnerID=8YFLogxK
dc.identifier.other PURE LINK: https://arxiv.org/abs/1811.10470
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36838
dc.description.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. en
dc.format.extent 9
dc.format.extent 3784-3792
dc.language.iso en en
dc.relation.ispartof IEEE International Conference on Big Data en
dc.relation.ispartofseries 2018 IEEE International Conference on Big Data (Big Data) en
dc.rights embargoedAccess en
dc.subject.other Statistics and Probability en
dc.subject.other Artificial Intelligence en
dc.subject.other 112 Statistics and probability en
dc.title Analysis of large sparse graphs using regular decomposition of graph distance matrices en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department VTT Technical Research Centre of Finland
dc.contributor.department Department of Mathematics and Systems Analysis
dc.contributor.department Ca Foscari University of Venice
dc.subject.keyword Statistics and Probability
dc.subject.keyword Artificial Intelligence
dc.subject.keyword 112 Statistics and probability
dc.identifier.urn URN:NBN:fi:aalto-201902251995
dc.identifier.doi 10.1109/BigData.2018.8622118
dc.date.embargo info:eu-repo/date/embargoEnd/2020-01-24


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