Learning Centrality By Learning To Route

The code base for the paper "Learning Centrality by Learning to Route." International Conference on Complex Networks and Their Applications. Springer, Cham, 2021.

Prerequisites

The code was implemented in python 3.7 with anaconda environment. All requirements are included in the requirements.txt file.

Components

RBC

Computing Routing Betweenness Centrality (RBC) of graph.

LRC

LRCNN

The neural-network with the forward flow logic.

ModelHandler

Hnadling the training of the model.

ModelTester

The main flow of LRC, in charge of intialization, training and computing the correlation scores.

Utils

CommonStr

String Constant

Optimizer

Wrapper for optimizer initialization

ParamsManager

Managing model parameters

Citation

In case of mentionaning the paper please use the following bibtex citation:

@inproceedings{bachar2021learning,
  title={Learning Centrality by Learning to Route},
  author={Bachar, Liav and Elyashar, Aviad and Puzis, Rami},
  booktitle={International Conference on Complex Networks and Their Applications},
  pages={247--259},
  year={2021},
  organization={Springer}
}