
TL;DR
This paper surveys recent advances in metrics for dense graphs and explores potential generalizations to sparse graphs, highlighting the complexities and open problems in extending the theory.
Contribution
It provides a survey of existing results on graph metrics and random graph models, and investigates their extension to sparse graphs, including many partial results and conjectures.
Findings
Metrics for dense graphs are well-understood and equivalent.
Connections between metrics and random graph models are complex in sparse graphs.
Many open problems and conjectures remain in the theory of sparse graph metrics.
Abstract
Recently, Bollob\'as, Janson and Riordan introduced a very general family of random graph models, producing inhomogeneous random graphs with edges. Roughly speaking, there is one model for each {\em kernel}, i.e., each symmetric measurable function from to the non-negative reals, although the details are much more complicated. A different connection between kernels and random graphs arises in the recent work of Borgs, Chayes, Lov\'asz, S\'os, Szegedy and Vesztergombi. They introduced several natural metrics on dense graphs (graphs with vertices and edges), showed that these metrics are equivalent, and gave a description of the completion of the space of all graphs with respect to any of these metrics in terms of {\em graphons}, which are essentially bounded kernels. One of the most appealing aspects of this work is the message that sequences of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeometry and complex manifolds · Geometric Analysis and Curvature Flows
