Network Inference via the Time-Varying Graphical Lasso

@article{Hallac2017NetworkIV,
  title={Network Inference via the Time-Varying Graphical Lasso},
  author={David Hallac and Youngsuk Park and Stephen P. Boyd and Jure Leskovec},
  journal={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2017}
}
  • David HallacYoungsuk Park J. Leskovec
  • Published 6 March 2017
  • Computer Science
  • Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time… 

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