PATENet: Pairwise Alignment of Time Evolving Networks

Overview

PATENet finds the best local alignment between two [temporal] sequences of objects (e.g. ordered sequences of networks) based on provided similarity measure between the objects comprising the sequences (e.g. networks), a monotone transform function, and an object-match threshold.

This is a python implementation of PATENet as described in

Gur, S., & Honavar, V. G. (2018, July). PATENet: Pairwise Alignment of Time Evolving Networks. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 85-98). Springer, Cham.

Please cite this paper if you use this code.

This implementation requires python>=3.6.

References

Gur, S., & Honavar, V. G. (2018, July). PATENet: Pairwise Alignment of Time Evolving Networks. In International Conference on Machine Learning and Data Mining in Pattern Recognition (pp. 85-98). Springer, Cham.