Phase transitions in spectral community detection of large noisy networks

In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probabi...

Full description

Saved in:
Bibliographic Details
Published in2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3402 - 3406
Main Authors Pin-Yu Chen, Hero, Alfred O.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Communities
community detectability
Noise measurement
noisy graph
Online AccessGet full text

Cover

More Information
Summary:In this paper, we study the sensitivity of the spectral clustering based community detection algorithm subject to a Erdos-Renyi type random noise model. We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general network model where two arbitrarily connected communities are interconnected by random external edges. Specifically, the community detection performance transitions from almost perfect detectability to low detectability as the intercommunity edge connection probability exceeds some critical value.We derive upper and lower bounds on the critical value and show that the bounds are identical when the two communities have the same size. The phase transition results are validated using network simulations. Using the derived expressions for the phase transition threshold we propose a method for estimating this threshold from observed data.
ISSN:1520-6149
DOI:10.1109/ICASSP.2015.7178602