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Inferring functional connectivity from time-series of events in large scale network deployments
Version 2 2023-06-07, 08:26
Version 1 2023-06-07, 06:41
journal contribution
posted on 2023-06-07, 08:26 authored by Antoine Messager, George ParisisGeorge Parisis, Istvan Kiss, Robert Harper, Philip Tee, Luc BerthouzeLuc BerthouzeTo respond rapidly and accurately to network and service outages, network operators must deal with a large number of events resulting from the interaction of various services operating on complex, heterogeneous and evolving networks. In this paper, we introduce the concept of functional connectivity as an alternative approach to monitoring those events. Commonly used in the study of brain dynamics, functional connectivity is defined in terms of the presence of statistical dependencies between nodes. Although a number of techniques exist to infer functional connectivity in brain networks, their straightforward application to commercial network deployments is severely challenged by: (a) non-stationarity of the functional connectivity, (b) sparsity of the time-series of events, and (c) absence of an explicit model describing how events propagate through the network or indeed whether they propagate. Thus, in this paper, we present a novel inference approach whereby two nodes are defined as forming a functional edge if they emit substantially more coincident or short-lagged events than would be expected if they were statistically independent. The output of the method is an undirected weighted graph, where the weight of an edge between two nodes denotes the strength of the statistical dependence between them. We develop a model of time-varying functional connectivity whose parameters are determined by maximising the model's predictive power from one time window to the next. We assess the accuracy, efficiency and scalability of our method on two real datasets of network events spanning multiple months and on synthetic data for which ground truth is available. We compare our method against both a general-purpose time-varying network inference method and network management specific causal inference technique and discuss its merits in terms of sensitivity, accuracy and, importantly, scalability.
Funding
A fast method for calculating the proximity matrix in a large-scale dynamic network; G1742; MOOGSOFT INC; Agreement dated 17 December 2014
History
Publication status
- Published
File Version
- Published version
Journal
IEEE Transactions on Network and Service ManagementISSN
1932-4537Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Issue
3Volume
16Page range
857-870Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2019-08-05First Open Access (FOA) Date
2019-08-05First Compliant Deposit (FCD) Date
2019-08-04Usage metrics
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