Abstract
Many algorithms for analyzing social networks assume that the structure of the network is known, but this is not always a reasonable assumption. We wish to reconstruct an underlying network given data about how some property, such as disease, has spread through the network. Properties may spread through a network in different ways: for instance, an individual may learn information as soon as one of his neighbors has learned that information, but political beliefs may follow a different type of model. We create algorithms for discovering underlying networks that would give rise to the diffusion in these models.
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Soundarajan, S., Hopcroft, J.E. (2010). Recovering Social Networks from Contagion Information. In: Kratochvíl, J., Li, A., Fiala, J., Kolman, P. (eds) Theory and Applications of Models of Computation. TAMC 2010. Lecture Notes in Computer Science, vol 6108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13562-0_38
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DOI: https://doi.org/10.1007/978-3-642-13562-0_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13561-3
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