  We consider the problem of inferring the functional connectivity of a
large-scale computer network from sparse time series of events emitted by its
nodes. We do so under the following three domain-specific constraints: (a)
non-stationarity of the functional connectivity due to unknown temporal changes
in the network, (b) sparsity of the time-series of events that limits the
effectiveness of classical correlation-based analysis, and (c) lack of an
explicit model describing how events propagate through the network. Under the
assumption that the probability of two nodes being functionally connected
correlates with the mean delay between their respective events, we develop an
inference method whose output is an undirected weighted network where the
weight of an edge between two nodes denotes the probability of these nodes
being functionally connected. Using a combination of windowing and convolution
to calculate at each time window a score quantifying the likelihood of a pair
of nodes emitting events in quick succession, we develop a model of
time-varying connectivity whose parameters are determined by maximising the
model's predictive power from one time window to the next. To assess the
effectiveness of our inference method, we construct synthetic data for which
ground truth is available and use these data to benchmark our approach against
three state-of-the-art inference methods. We conclude by discussing its
application to data from a real-world large-scale computer network.
