Abstract
In this brief announcement we propose a distributed algorithm to assess the connectivity quality of a network, be it physical or logical. In large complex networks, some nodes may play a vital role due to their position (e.g. for routing or network reliability). Assessing global properties of a graph, as importance of nodes, usually involves lots of communications; doing so while keeping the overhead low is an open challenge. To that end, centrality notions have been introduced by researchers (see e.g. [Fre77]) to rank the nodes as a function of their importance in the topology. Some of these techniques are based on computing the ratio of shortest-paths that pass through any graph node. This approach has a limitation as nodes “close” from the shortest-paths do not get a better score than any other arbitrary ones. To avoid this drawback, physician Newman proposed a centralized measure of betweenness centrality [New03] based on random walks: counting for each node the number of visits of a random walk travelling from a node i to a target node j, and then averaging this value by all graph source/target pairs. Yet this approach relies on the full knowledge of the graph for each system node, as the random walk target node should be known by advance; this is not an option in large-scale networks. We propose a distributed solution that relies on a single random walk travelling in the graph; each node only needs to be aware of its topological neighbors to forward the walk.
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© 2008 Springer-Verlag Berlin Heidelberg
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Kermarrec, AM., Le Merrer, E., Sericola, B., Trédan, G. (2008). Evaluating the Quality of a Network Topology through Random Walks. In: Taubenfeld, G. (eds) Distributed Computing. DISC 2008. Lecture Notes in Computer Science, vol 5218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87779-0_40
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DOI: https://doi.org/10.1007/978-3-540-87779-0_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87778-3
Online ISBN: 978-3-540-87779-0
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