short-paper

Influence of the Null-Model on Motif Detection

Authors:

Wolfgang E. Schlauch,

Katharina A. Zweig,

Network AnalysisAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 514 - 519

https://doi.org/10.1145/2808797.2809400

Published: 25 August 2015 Publication History

Abstract

This paper focuses on the suitability of three different null-models to motif analysis that all get as an input a desired degree sequence. A graph theoretic null-model is defined as a set of graphs together with a probability function. Here we discuss the configuration model, as the simplest model; a variant of the configuration model where multi-edges are deleted; and the set of all graphs with a given degree sequence (FDSM), that most scientists would recommend to use but that has the disadvantage of a high time-complexity to sample from it. Furthermore, we develop equations for the expected number of motifs in the FDSM, based on the degree sequence and the assumption of simple independence. We present the motif count for several real-world graphs and compare them with the sampled average number of these motif counts in the different null-models. We check with a Kolmogorov-Smirnow two-sample test whether the samples originated from the same distribution. It can then be shown that the motif counts in the configuration model do not coincide with those of the FDSM. The equations are a good enough approximation of the motif count in generated graphs based on a prescribed degree sequence.

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Cited By

Zhang DKabuka M(2024)MARML: Motif-Aware Deep Representation Learning in Multilayer NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334134735:9(11649-11660)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3341347
Preti GFazzone APetri GDe Francisci Morales G(2024)Higher-Order Null Models as a Lens for Social SystemsPhysical Review X10.1103/PhysRevX.14.03103214:3Online publication date: 20-Aug-2024
https://doi.org/10.1103/PhysRevX.14.031032
Allendorf DMeyer UPenschuck MTran H(2023)Parallel global edge switching for the uniform sampling of simple graphs with prescribed degreesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.12.010174:C(118-129)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jpdc.2022.12.010
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Influence of the Null-Model on Motif Detection
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 August 2015

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Deutsche Forschungsgemeinschaft

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ASONAM '15

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SIGKDD

ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Zhang DKabuka M(2024)MARML: Motif-Aware Deep Representation Learning in Multilayer NetworksIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334134735:9(11649-11660)Online publication date: Sep-2024
https://doi.org/10.1109/TNNLS.2023.3341347
Preti GFazzone APetri GDe Francisci Morales G(2024)Higher-Order Null Models as a Lens for Social SystemsPhysical Review X10.1103/PhysRevX.14.03103214:3Online publication date: 20-Aug-2024
https://doi.org/10.1103/PhysRevX.14.031032
Allendorf DMeyer UPenschuck MTran H(2023)Parallel global edge switching for the uniform sampling of simple graphs with prescribed degreesJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.12.010174:C(118-129)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.jpdc.2022.12.010
Chinazzo ADe Schryver CZweig KWehn N(2023)Increasing the Sampling Efficiency for the Link Assessment ProblemAlgorithms for Big Data10.1007/978-3-031-21534-6_3(39-56)Online publication date: 18-Jan-2023
https://doi.org/10.1007/978-3-031-21534-6_3
Meyer UPenschuck M(2023)Generating Synthetic Graph Data from Random Network ModelsAlgorithms for Big Data10.1007/978-3-031-21534-6_2(21-38)Online publication date: 18-Jan-2023
https://doi.org/10.1007/978-3-031-21534-6_2
Allendorf DMeyer UPenschuck MTran H(2022)Parallel Global Edge Switching for the Uniform Sampling of Simple Graphs with Prescribed Degrees2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00034(269-279)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00034
Yu SXia FSun YTang TYan XLee I(2021)Detecting Outlier Patterns With Query-Based Artificially Generated Searching ConditionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29779588:1(134-147)Online publication date: Feb-2021
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Al-Shaikh AMahafzah BAlshraideh M(2021)RETRACTED ARTICLE: Hybrid harmony search algorithm for social network contact tracing of COVID-19Soft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-05948-227:6(3343-3365)Online publication date: 28-Jun-2021
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Kušen EStrembeck M(2019)An analysis of emotion-exchange motifs in multiplex networks during emergency eventsApplied Network Science10.1007/s41109-019-0115-64:1Online publication date: 21-Mar-2019
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