@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 = jan,
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",
note = "14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 ; Conference date: 15-07-2018 Through 19-07-2018",
}