Abstract
Completing their initial phase of rapid growth social networks are expected to reach a plateau from where on they are in a statistically stationary state. Such stationary conditions may have different dynamical properties. For example, if each message in a network is followed by a reply in opposite direction, the dynamics is locally balanced. Otherwise, if messages are ignored or forwarded to a different user, one may reach a stationary state with a directed flow of information. To distinguish between the two situations, we propose a quantity called entropy production that was introduced in statistical physics as a measure for non-vanishing probability currents in nonequilibrium stationary states. The proposed quantity closes a gap for characterizing social networks. As major contribution, we present a general scheme that allows one to measure the entropy production in arbitrary social networks in which individuals are interacting with each other, e.g. by exchanging messages. The scheme is then applied for a specific example of the R mailing list.
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Hinrichsen, H., Hoßfeld, T., Hirth, M., Tran-Gia, P. (2013). Entropy Production in Stationary Social Networks. In: Ghoshal, G., Poncela-Casasnovas, J., Tolksdorf, R. (eds) Complex Networks IV. Studies in Computational Intelligence, vol 476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36844-8_5
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DOI: https://doi.org/10.1007/978-3-642-36844-8_5
Publisher Name: Springer, Berlin, Heidelberg
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