  In this paper, we consider the problem of recovering a graph that represents
the statistical data dependency among nodes for a set of data samples generated
by nodes, which provides the basic structure to perform an inference task, such
as MAP (maximum a posteriori). This problem is referred to as structure
learning. When nodes are spatially separated in different locations, running an
inference algorithm requires a non-negligible amount of message passing,
incurring some communication cost. We inevitably have the trade-off between the
accuracy of structure learning and the cost we need to pay to perform a given
message-passing based inference task because the learnt edge structures of data
dependency and physical connectivity graph are often highly different. In this
paper, we formalize this trade-off in an optimization problem which outputs the
data dependency graph that jointly considers learning accuracy and
message-passing costs. We focus on a distributed MAP as the target inference
task, and consider two different implementations, ASYNC-MAP and SYNC-MAP that
have different message-passing mechanisms and thus different cost structures.
In ASYNC- MAP, we propose a polynomial time learning algorithm that is optimal,
motivated by the problem of finding a maximum weight spanning tree. In
SYNC-MAP, we first prove that it is NP-hard and propose a greedy heuristic. For
both implementations, we then quantify how the probability that the resulting
data graphs from those learning algorithms differ from the ideal data graph
decays as the number of data samples grows, using the large deviation
principle, where the decaying rate is characterized by some topological
structures of both original data dependency and physical connectivity graphs as
well as the degree of the trade-off. We validate our theoretical findings
through extensive simulations, which confirms that it has a good match.

Machine Learning;Machine Learning