Moment-based parameter estimation in binomial random intersection graph models

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

Researchers

Research units

Abstract

Binomial random intersection graphs can be used as parsimonious statistical models of large and sparse networks, with one parameter for the average degree and another for transitivity, the tendency of neighbours of a node to be connected. This paper discusses the estimation of these parameters from a single observed instance of the graph, using moment estimators based on observed degrees and frequencies of 2-stars and triangles. The observed data set is assumed to be a subgraph induced by a set of n0 nodes sampled from the full set of n nodes. We prove the consistency of the proposed estimators by showing that the relative estimation error is small with high probability for n0 ≫ n2/3 ≫ 1. As a byproduct, our analysis confirms that the empirical transitivity coefficient of the graph is with high probability close to the theoretical clustering coefficient of the model.

Details

Original languageEnglish
Title of host publicationAlgorithms and Models for the Web Graph
Subtitle of host publication14th International Workshop, WAW 2017, Toronto, ON, Canada, June 15–16, 2017, Revised Selected Papers
EditorsAnthony Bonato, Fan Chung Graham, Paweł Prałat
Publication statusPublished - 6 Sep 2017
MoE publication typeA4 Article in a conference publication
EventWorkshop on Algorithms and Models for the Web Graph - Fields Institute for Research in Mathematical Sciences, Toronto, Canada
Duration: 15 Jun 201716 Jun 2017
Conference number: 14
http://www.math.ryerson.ca/waw2017/index.html

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10519
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopWorkshop on Algorithms and Models for the Web Graph
Abbreviated titleWAW
CountryCanada
CityToronto
Period15/06/201716/06/2017
Internet address

    Research areas

  • statistical network model, network motif, model fitting, sparse graph, moment estimator, two-mode network, overlapping communities

ID: 13005931