Functional topology inference from network events

Messager, Antoine, Parisis, Georgios, Kiss, István Z, Harper, Robert, Tee, Phil and Berthouze, Luc (2018) Functional topology inference from network events. IFIP/IEEE International Symposium on Integrated Network Management. Intelligent Management for the Next Wave of Cyber and Social Networks, Washington DC, USA, 8-12 April 2019. Published in: Proceedings of the 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM 2019). Institute of Electrical and Electronics Engineers ISBN 9783903176157 (Accepted)

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Abstract

In this paper we present a novel approach for inferring functional connectivity within a large-scale network from time series of emitted node events. We do so under the following constraints: (a) non-stationarity of the underlying connectivity, (b) sparsity of the time-series of events, and (c) absence of an explicit model describing how events propagate through the network. 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. Two nodes are assumed to be functionally connected if they show significantly more coincident or short-lagged events than randomly picked pairs of nodes with similar levels of activity. 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. We assess the accuracy, efficiency and scalability of our method on a real dataset of network events spanning multiple months.

Item Type: Conference Proceedings
Keywords: network management, network events, topology inference, functional connectivity, machine learning
Schools and Departments: School of Engineering and Informatics > Informatics
School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Sussex Neuroscience
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5 Computer networks. General works
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Depositing User: Luc Berthouze
Date Deposited: 16 Jan 2019 14:19
Last Modified: 12 Feb 2019 16:56
URI: http://srodev.sussex.ac.uk/id/eprint/81305

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A fast method for calculating the proximity matrix in a large-scale dynamic networkG1742MOOGSOFT INCAgreement dated 17 December 2014