Modelling Zeros in Blockmodelling

Laurence A.F. Park*, Mohadeseh Ganji, Emir Demirovic, Jeffrey Chan, Peter Stuckey, James Bailey, Christopher Leckie, Rao Kotagiri

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Blockmodelling is the process of determining community structure in a graph. Real graphs contain noise and so it is up to the blockmodelling method to allow for this noise and reconstruct the most likely role memberships and role relationships. Relationships are encoded in a graph using the absence and presence of edges. Two objects are considered similar if they each have edges to a third object. However, the information provided by missing edges is ambiguous and therefore can be measured in different ways. In this article, we examine the effect of the choice of block metric on blockmodelling accuracy and find that data relationships can be position based or set based. We hypothesise that this is due to the data containing either Hamming noise or Jaccard noise. Experiments performed on simulated data show that when no noise is present, the accuracy is independent of the choice of metric. But when noise is introduced, high accuracy results are obtained when the choice of metric matches the type of noise.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Proceedings
EditorsJoão Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages187-198
Number of pages12
ISBN (Print)978-3-031-05936-0
DOIs
Publication statusPublished - 2022
Event26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022 - Chengdu, China
Duration: 16 May 202219 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13281 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2022
Country/TerritoryChina
CityChengdu
Period16/05/2219/05/22

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