TY - GEN
T1 - PATENet
T2 - 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
AU - Gur, Shlomit
AU - Honavar, Vasant G.
N1 - Funding Information:
Acknowledgments. This project was supported in part by the National Center for Advancing Translational Sciences, National Institutes of Health through Grant UL1 TR000127 and TR002014, by the National Science Foundation, through Grant SHF 1518732, the Center for Big Data Analytics and Discovery Informatics at Pennsylvania State University, the Edward Fry-moyer Endowed Professorship in Information Sciences and Technology at Pennsylvania State University and the Sudha Murty Distinguished Visiting Chair in Neurocomputing and Data Science funded by the Pratiksha Trust at the Indian Institute of Science [both held by Vasant Honavar]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors. We thank Sanghack Lee for helpful discussions during the course of this work.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
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
Y2 - 15 July 2018 through 19 July 2018
ER -