@inproceedings{f074edbfc0a34e32b88c702e7d2e43ef,
title = "Graph representation using mutual information for graph model discrimination",
abstract = "We present a novel approach of graph representation based on mutual information of a random walk in a graph. This representation, as any global metric of a graph, can be used to identify the model generator of the observed network. In this study, we use our graph representation combined with Random Forest (RF) to discriminate between Erd{\"o}s-Renyi (ER), Stochastic Block Model (SBM) and Planted Clique (PC) models. We also combine our graph representation with a Squared Mahalanobis Distance (SMD)-based test to reject a model given an observed network. We test the proposed method with computer simulations.",
keywords = "Complex Networks, Graph Theory, Mutual Information, Network Topology",
author = "Francisco Hawas and Djuri{\'c}, \{Petar M.\}",
note = "Publisher Copyright: {\textcopyright} EURASIP 2018.; 26th European Signal Processing Conference, EUSIPCO 2018 ; Conference date: 03-09-2018 Through 07-09-2018",
year = "2018",
month = nov,
day = "29",
doi = "10.23919/EUSIPCO.2018.8553169",
language = "English",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
pages = "882--886",
booktitle = "2018 26th European Signal Processing Conference, EUSIPCO 2018",
}