Version 1.0.

This repository hosts the code for our IEEE ICDM 2017 paper and follow-up KAIS journal paper on inferring networks from time series efficiently:

Tara Safavi, Chandra Sripada, Danai Koutra. Scalable Hashing-Based Network Discovery. IEEE International Conference on Data Mining, 2017.

Tara Safavi, Chandra Sekhar Sripada, Danai Koutra: Fast network discovery on sequence data via time-aware hashing. Knowl. Inf. Syst. 61(2): 987-1017 (2019)

Link to the conference paper: https://gemslab.github.io/papers/safavi-2017-scalable.pdf

Link to the journal paper: https://gemslab.github.io/papers/safavi-2018-fast.pdf

@INPROCEEDINGS{SafaviSK17,
  author    = {T. {Safavi} and C. {Sripada} and D. {Koutra}},
  title     = {Scalable Hashing-Based Network Discovery}, 
  booktitle = {IEEE International Conference on Data Mining (ICDM)}, 
  year      = {2017},
  pages     = {405-414},
  }

@INPROCEEDINGS{SafaviSK19journal,
  author    = {T. {Safavi} and C. {Sripada} and D. {Koutra}},
  title     = {Fast network discovery on sequence data via time-aware hashing}, 
  journal   = {Knowl. Inf. Syst.},
  volume    = {61},
  number    = {2},
  pages     = {987--1017},
  year      = {2019},
  }

To run, change directories to the discovery directory and type make. This will generate a sample graph from synthetic data using the pairwise correlation and window LSH methods.

If you have questions about the code, please email Tara Safavi at [email protected].