English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

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