TY - JOUR
T1 - Network Reconstruction and Community Detection from Dynamics
AU - Peixoto, Tiago P.
N1 - Publisher Copyright:
© 2019 American Physical Society.
PY - 2019/9/18
Y1 - 2019/9/18
N2 - We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.
AB - We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network. We show that the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. We illustrate the use of our method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.
UR - http://www.scopus.com/inward/record.url?scp=85072805627&partnerID=8YFLogxK
U2 - 10.1103/PhysRevLett.123.128301
DO - 10.1103/PhysRevLett.123.128301
M3 - Article
C2 - 31633974
AN - SCOPUS:85072805627
SN - 0031-9007
VL - 123
JO - Physical Review Letters
JF - Physical Review Letters
IS - 12
M1 - 128301
ER -