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Functional Topology Inference from Network Events | IEEE Conference Publication | IEEE Xplore

Functional Topology Inference from Network Events

Publisher: IEEE

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 f...View more

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.
Date of Conference: 08-12 April 2019
Date Added to IEEE Xplore: 20 May 2019
ISBN Information:
Print on Demand(PoD) ISSN: 1573-0077
Publisher: IEEE
Conference Location: Arlington, VA, USA

References

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