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Learning Multifractal Structure in Large Networks
Published on Oct 07, 20141633 Views
Using random graphs to model networks has a rich history. In this paper, we analyze and improve the multifractal network generators (MFNG) introduced by Palla et al. We provide a new result on the pro
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Chapter list
Learning Multifractal Structure in Large Networks00:00
Setting00:10
Our contributions - 101:02
Our contributions - 201:20
An introduction to Multifractal Network Generators (MFNG)02:10
Generating a graph with no recursion02:57
From line to square03:37
Adding recursion - 104:12
Adding recursion - 204:39
Expanding the recursion05:09
Main theoretical result05:58
Computing expected number of edges is easy06:52
Computing moments of certain subgraphs is easy07:38
We can learn multifractal structure quickly07:51
Method of moments recovers small synthetic graphs08:59
Twitter network09:55
Citation network10:51
Fast sampling is challenging11:34
Conclusion12:42