PATENet: Pairwise Alignment of Time Evolving Networks

  • Shlomit GurEmail author
  • Vasant G. Honavar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)


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.


Network science Multilayer networks Temporal alignment 



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 Frymoyer 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.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Huck Institutes of the Life SciencesPennsylvania State UniversityUniversity ParkUSA
  2. 2.College of Information Sciences and TechnologyPennsylvania State UniversityUniversity ParkUSA

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