PATENet: Pairwise Alignment of Time Evolving Networks

Shlomit Gur, Vasant G. Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Networks that change over time, e.g. functional brain networks that change their structure due to processes such as development or aging, are naturally modeled by time-evolving networks. In this paper we present PATENet, a novel method for aligning time-evolving networks. PATENet offers a mathematically-sound approach to aligning time evolving networks. PATENet leverages existing similarity measures for networks with fixed topologies to define well-behaved similarity measures for time evolving networks. We empirically explore the behavior of PATENet through synthetic time evolving networks under a variety of conditions.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages85-98
Number of pages14
ISBN (Print)9783319961354
DOIs
StatePublished - Jan 1 2018
Event14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, United States
Duration: Jul 15 2018Jul 19 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10934 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
CountryUnited States
CityNew York
Period7/15/187/19/18

Fingerprint

Pairwise
Brain
Alignment
Aging of materials
Topology
Acoustic waves
Similarity Measure
Leverage

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gur, S., & Honavar, V. G. (2018). PATENet: Pairwise Alignment of Time Evolving Networks. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings (pp. 85-98). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10934 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-96136-1_8
Gur, Shlomit ; Honavar, Vasant G. / PATENet : Pairwise Alignment of Time Evolving Networks. Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings. editor / Petra Perner. Springer Verlag, 2018. pp. 85-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{9572d935325043339c2e30e7b3255c3e,
title = "PATENet: Pairwise Alignment of Time Evolving Networks",
abstract = "Networks that change over time, e.g. functional brain networks that change their structure due to processes such as development or aging, are naturally modeled by time-evolving networks. In this paper we present PATENet, a novel method for aligning time-evolving networks. PATENet offers a mathematically-sound approach to aligning time evolving networks. PATENet leverages existing similarity measures for networks with fixed topologies to define well-behaved similarity measures for time evolving networks. We empirically explore the behavior of PATENet through synthetic time evolving networks under a variety of conditions.",
author = "Shlomit Gur and Honavar, {Vasant G.}",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-96136-1_8",
language = "English (US)",
isbn = "9783319961354",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "85--98",
editor = "Petra Perner",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings",
address = "Germany",

}

Gur, S & Honavar, VG 2018, PATENet: Pairwise Alignment of Time Evolving Networks. in P Perner (ed.), Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10934 LNAI, Springer Verlag, pp. 85-98, 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, New York, United States, 7/15/18. https://doi.org/10.1007/978-3-319-96136-1_8

PATENet : Pairwise Alignment of Time Evolving Networks. / Gur, Shlomit; Honavar, Vasant G.

Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings. ed. / Petra Perner. Springer Verlag, 2018. p. 85-98 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10934 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - PATENet

T2 - Pairwise Alignment of Time Evolving Networks

AU - Gur, Shlomit

AU - Honavar, Vasant G.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Networks that change over time, e.g. functional brain networks that change their structure due to processes such as development or aging, are naturally modeled by time-evolving networks. In this paper we present PATENet, a novel method for aligning time-evolving networks. PATENet offers a mathematically-sound approach to aligning time evolving networks. PATENet leverages existing similarity measures for networks with fixed topologies to define well-behaved similarity measures for time evolving networks. We empirically explore the behavior of PATENet through synthetic time evolving networks under a variety of conditions.

AB - Networks that change over time, e.g. functional brain networks that change their structure due to processes such as development or aging, are naturally modeled by time-evolving networks. In this paper we present PATENet, a novel method for aligning time-evolving networks. PATENet offers a mathematically-sound approach to aligning time evolving networks. PATENet leverages existing similarity measures for networks with fixed topologies to define well-behaved similarity measures for time evolving networks. We empirically explore the behavior of PATENet through synthetic time evolving networks under a variety of conditions.

UR - http://www.scopus.com/inward/record.url?scp=85050536246&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050536246&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-96136-1_8

DO - 10.1007/978-3-319-96136-1_8

M3 - Conference contribution

AN - SCOPUS:85050536246

SN - 9783319961354

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 85

EP - 98

BT - Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings

A2 - Perner, Petra

PB - Springer Verlag

ER -

Gur S, Honavar VG. PATENet: Pairwise Alignment of Time Evolving Networks. In Perner P, editor, Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings. Springer Verlag. 2018. p. 85-98. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-96136-1_8