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Titlebook: Health Services for Cancer Survivors; Practice, Policy and Michael Feuerstein,Patricia A. Ganz Book 2011 Springer Science+Business Media, L

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Robyn Osborn,Tricia L. Psota,Jaesin Sa,Tracy Sbroccoample size and breakdowns during numerical implementation and less sensitive to uncertainty in connectivity. These features are especially important in the context of larger data sets or when the data may contain errors in the connectivity patterns.
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Kevin C. Oeffinger,Larissa Nekhlyudovsts of nodes with high probability and with computational savings of orders of magnitude. We also propose stopping criteria for the random walk method which requires very little knowledge about the structure of the network..Õ(. .)
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Mary S. McCabe,Scott K. Baker,Caroline Huffman,Ken Millerl parameters. In particular, the model turns out to be able to reflect realistically these two quantitative characteristics of a real network, thus performing better than previous preferential attachment models. All our theoretical results are illustrated empirically.
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Jennifer Malin,Emma-Jane Sayers,Michael Jefford algorithms and applications that exploit these graphs. The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.
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