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Data-Driven Model Reduction, Scientific Frontiers, and Applications

Mahmood Ettehad, Department of Mathematics
Graph Reconstruction from Path Correlation Data


  • Gregory Berkolaiko
  • Nick Duffield
  • Mahmood Ettehad
  • Kyriakos Manousakis


A communication network can be modeled as a directed connected graph with edge weights that characterize performance metrics such as loss and delay. Network tomography aims to infer these edge weights from their pathwise versions measured on a set of intersecting paths between a subset of boundary vertices, and even the underlying graph when this is not known. In particular, temporal correlations between path metrics have been used infer composite weights on the subpath formed by the path intersection. We call these subpath weights the Path Correlation Data.

In this work we answer the following question: when can the underlying weighted graph be recovered knowing only the boundary vertices and the Path Correlation Data?