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Title	Recognizing graphs with linear random structure
Creator	Janssen, Jeannette
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2016-11-08T11:13
Description	In many real life applications, network formation can be modelled using a spatial random graph model: vertices are embedded in a metric space S, and pairs of vertices are more likely to be connected if they are closer together in the space. A general geometric graph model that captures this concept is G(n, w), where w : S × S → [0, 1] is a symmetric “link probability” function with the property that, for fixed x ∈ S, w(x, y) decreases as y is moved further away from x. he function w can be seen as the graph limit of the sequence G(n, w) as n → ∞. We consider the question: given a large graph or sequence of graphs, how can we determine if they are likely the results of such a general geometric random graph process? Focusing on the one-dimensional (linear) case where S = [0, 1], we define a graph parameter Γ and use the theory of graph limits to show that this parameter indeed measures the compatibility of the graph with a linear model.
Extent	30 minutes
Subject	Mathematics Combinatorics Probability theory and stochastic processes
Geographic Location	Banff (Alta.)
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Dalhousie University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2017-05-09
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0347366
URI	http://hdl.handle.net/2429/61569
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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