Abstract
In many areas of science huge networks (graphs) are central objects of study: the internet, the brain, various social networks, VLSI, statistical physics. To study these graphs, new paradigms are needed: What are meaningful questions to ask? When are two huge graphs “similar”? How to “scale down” these graphs without changing their fundamental structure and algorithmic properties? How to generate random examples with the desired properties? A reasonably complete answer can be given in the case when the huge graphs are dense (in the more difficult case of sparse graphs there are only partial results).
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© 2007 Springer-Verlag Berlin Heidelberg
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Lovász, L. (2007). Approximating Graphs by Graphs and Functions (Abstract). In: Csuhaj-Varjú, E., Ésik, Z. (eds) Fundamentals of Computation Theory. FCT 2007. Lecture Notes in Computer Science, vol 4639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74240-1_3
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DOI: https://doi.org/10.1007/978-3-540-74240-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74239-5
Online ISBN: 978-3-540-74240-1
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