Inferring the time-varying functional connectivity of large-scale computer networks from emitted events

TL;DR

This paper introduces a novel method to infer dynamic functional connectivity in large-scale computer networks from sparse event data, addressing non-stationarity, sparsity, and lack of explicit propagation models.

Contribution

The paper presents a new inference approach that models time-varying connectivity using event delays, outperforming existing methods on synthetic benchmarks and applicable to real-world networks.

Findings

Effective inference of dynamic connectivity from sparse data

Outperforms three state-of-the-art methods on synthetic benchmarks

Applicable to real-world large-scale computer networks

Abstract

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.