Publication details for Dr George MertziosEnright, J., Meeks, K., Mertzios, G.B. & Zamaraev, V. (2019), Deleting edges to restrict the size of an epidemic in temporal networks, in Rossmanith, Peter Heggernes, Pinar & Katoen, Joost-Pieter eds, LIPIcs (Leibniz International Proceedings in Informatics) 138: 44th International Symposium on Mathematical Foundations of Computer Science (MFCS). Aachen, Germany, Schloss Dagstuhl, Saarbrücken/Wadern, 57:1--57:15.
- Publication type: Conference Paper
- ISSN/ISBN: 1868-8969, 9783959771177
- DOI: 10.4230/LIPIcs.MFCS.2019.57
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
Spreading processes on graphs are a natural model for a wide variety of real-world phenomena, including information or behaviour spread over social networks, biological diseases spreading over contact or trade networks, and the potential flow of goods over logistical infrastructure. Often, the networks over which these processes spread are dynamic in nature, and can be modeled with graphs whose structure is subject to discrete changes over time, i.e. with temporal graphs. Here, we consider temporal graphs in which edges are available at specified timesteps, and study the problem of deleting edges from a given temporal graph in order to reduce the number of vertices (temporally) reachable from a given starting point. This could be used to control the spread of a disease, rumour, etc. in a temporal graph. In particular, our aim is to find a temporal subgraph in which a process starting at any single vertex can be transferred to only a limited number of other vertices using a temporally-feasible path (i.e. a path, along which the times of the edge availabilities increase). We introduce a natural deletion problem for temporal graphs and we provide positive and negative results on its computational complexity, both in the traditional and the parameterised sense (subject to various natural parameters), as well as addressing the approximability of this problem.