Developing techniques to infer the behavior of networked social systems has attracted a
lot of attention in the literature. Using a discrete dynamical system to model a networked
social system, the problem of inferring the behavior of the system can be formulated as the
problem of learning the local functions of the dynamical system. We investigate the problem
assuming an active form of interaction with the system through queries. We consider two
classes of local functions (namely, symmetric and threshold functions) and two interaction
modes, namely batch (where all the queries must be submitted together) and adaptive
(where the set of queries submitted at a stage may rely on the answers to previous queries).
We establish bounds on the number of queries under both batch and adaptive query modes
using vertex coloring and probabilistic methods. Our results show that a small number of
appropriately chosen queries are provably sufficient to correctly learn all the local functions.
We develop complexity results which suggest that, in general, the problem of generating
query sets of minimum size is computationally intractable. We present efficient heuristics
that produce query sets under both batch and adaptive query modes. Also, we present a query compaction algorithm that identifies and removes redundant queries from a given
query set. Our algorithms were evaluated through experiments on over 20 wellknown
networks.
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Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries
Many 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 realworld networks.
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 NSFPAR ID:
 10376922
 Date Published:
 Journal Name:
 International Conference on Machine Learning
 Page Range / eLocation ID:
 1879618808
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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