TY - JOUR

T1 - Inferring network properties based on the epidemic prevalence

AU - Ma, Long

AU - Liu, Qiang

AU - Van Mieghem, Piet

PY - 2019

Y1 - 2019

N2 - Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network properties can be determined from incomplete measurements of the dynamical process is still an open question. In this paper, we focus on the problem of inferring the properties of the underlying network from the dynamics of a susceptible-infected-susceptible epidemic and we assume that only a time series of the epidemic prevalence, i.e., the average fraction of infected nodes, is given. We find that some of the network metrics, namely those that are sensitive to the epidemic prevalence, can be roughly inferred if the network type is known. A simulated annealing link-rewiring algorithm, called SARA, is proposed to obtain an optimized network whose prevalence is close to the benchmark. The output of the algorithm is applied to classify the network types.

AB - Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network properties can be determined from incomplete measurements of the dynamical process is still an open question. In this paper, we focus on the problem of inferring the properties of the underlying network from the dynamics of a susceptible-infected-susceptible epidemic and we assume that only a time series of the epidemic prevalence, i.e., the average fraction of infected nodes, is given. We find that some of the network metrics, namely those that are sensitive to the epidemic prevalence, can be roughly inferred if the network type is known. A simulated annealing link-rewiring algorithm, called SARA, is proposed to obtain an optimized network whose prevalence is close to the benchmark. The output of the algorithm is applied to classify the network types.

KW - OA-Fund TU Delft

UR - http://www.scopus.com/inward/record.url?scp=85074263018&partnerID=8YFLogxK

U2 - 10.1007/s41109-019-0218-0

DO - 10.1007/s41109-019-0218-0

M3 - Article

AN - SCOPUS:85074263018

VL - 4

SP - 1

EP - 13

JO - Applied Network Science

JF - Applied Network Science

SN - 2364-8228

IS - 1

M1 - 93

ER -