Moog (United Kingdom)

A crucial step in remedying faults within network infrastructures is to determine their root cause. However, the large-scale, complex and dynamic nature of modern networks makes causal inference-based root cause analysis (RCA) challenging in terms of scalability and knowledge drift over time. In this paper, we propose a framework that utilises the neuroscientific concept of functional connectivity – a graph representation of statistical dependencies between events – as a scalable approach to acquire and maintain prior knowledge for causal inference-based RCA approaches in dynamic networks. We demonstrate on both synthetic and real world data that our proposed approach can provide significant speedups to existing causal inference approaches without significant loss of accuracy. Finally, we discuss the impact of the choice of user-defined parameters on causal inference accuracy and conclude that the framework can safely be deployed in the real world.

The structure of complex networks has long been understood to play a role in transmission and spreading phenomena on a graph. Such networks form an important part of the structure of society, including transportation networks. As society fights to control the COVID-19 pandemic, an important question is how to choose the optimum balance between the full opening of transport networks and the control of epidemic spread. In this work we investigate the interplay between network dismantling and epidemic spread rate as a proxy for the imposition of travel restrictions to control disease spread. For network dismantling we focus on the weighted and unweighted forms of metrics that capture the topological and informational structure of the network. Our results indicate that there is benefit to a directed approach to imposing travel restrictions, but we identify that more detailed models of the transport network are necessary for definitive results.