PATENet: Pairwise Alignment of Time Evolving Networks
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.
KeywordsNetwork 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.
- 12.Koutra, D., Vogelstein, J.T., Faloutsos, C.: DELTACON: a principled massive-graph similarity function. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 162–170. SIAM (2013). https://doi.org/10.1137/1.9781611972832.18
- 13.Lee, W.N., Das, A.K.: Local alignment tool for clinical history: temporal semantic search of clinical databases. In: AMIA Annual Symposium Proceedings, pp. 437–441 (2010)Google Scholar