PATENet: Pairwise Alignment of Time Evolving Networks

Shlomit Gur; Vasant G. Honavar

doi:10.1007/978-3-319-96136-1_8

PATENet: Pairwise Alignment of Time Evolving Networks

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
Editors	Petra Perner
Publisher	Springer Verlag
Pages	85-98
Number of pages	14
ISBN (Print)	9783319961354
DOIs	https://doi.org/10.1007/978-3-319-96136-1_8
State	Published - Jan 1 2018
Event	14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, United States Duration: Jul 15 2018 → Jul 19 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10934 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Country	United States
City	New York
Period	7/15/18 → 7/19/18

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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

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)).

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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 proceeding › Conference contribution

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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

Access to Document

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