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 | |
| 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
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PATENet : Pairwise Alignment of Time Evolving Networks. / Gur, Shlomit; Honavar, Vasant Gajanan.
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
TY - GEN
T1 - PATENet
T2 - Pairwise Alignment of Time Evolving Networks
AU - Gur, Shlomit
AU - Honavar, Vasant Gajanan
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.
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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
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 -