%ARosenkrantz, D.%AAdiga, A.%AMarathe, M.%AQiu, Z.%ARavi, S.%AStearns, R.%AVullikanti, A.%D2022%I
%K
%MOSTI ID: 10376922
%PMedium: X
%TEfficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries
%XMany papers have addressed the problem of learning
the behavior (i.e., the local interaction function
at each node) of a networked system through
active queries, assuming that the network topology
is known. We address the problem of inferring
both the network topology and the behavior
of such a system through active queries. Our results
are for systems where the state of each node
is from {0, 1} and the local functions are Boolean.
We present inference algorithms under both batch
and adaptive query models for dynamical systems
with symmetric local functions. These algorithms
show that the structure and behavior of such dynamical
systems can be learnt using only a polynomial
number of queries. Further, we establish
a lower bound on the number of queries needed
to learn such dynamical systems. We also present
experimental results obtained by running our algorithms
on synthetic and real-world networks.
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