1 research outputs found
Consistent Tomography under Partial Observations over Adaptive Networks
This work studies the problem of inferring whether an agent is directly
influenced by another agent over an adaptive diffusion network. Agent i
influences agent j if they are connected (according to the network topology),
and if agent j uses the data from agent i to update its online statistic. The
solution of this inference task is challenging for two main reasons. First,
only the output of the diffusion learning algorithm is available to the
external observer that must perform the inference based on these indirect
measurements. Second, only output measurements from a fraction of the network
agents is available, with the total number of agents itself being also unknown.
The main focus of this article is ascertaining under these demanding conditions
whether consistent tomography is possible, namely, whether it is possible to
reconstruct the interaction profile of the observable portion of the network,
with negligible error as the network size increases. We establish a critical
achievability result, namely, that for symmetric combination policies and for
any given fraction of observable agents, the interacting and non-interacting
agent pairs split into two separate clusters as the network size increases.
This remarkable property then enables the application of clustering algorithms
to identify the interacting agents influencing the observations. We provide a
set of numerical experiments that verify the results for finite network sizes
and time horizons. The numerical experiments show that the results hold for
asymmetric combination policies as well, which is particularly relevant in the
context of causation.Comment: Submitted for publicatio