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

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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