Graphons of Line Graphs
Not on the map. Why?
Abstract (arXiv)
We consider the problem of estimating graph limits, known as graphons, from observations of sequences of sparse finite graphs. In this paper we show a simple method that can shed light on a subset of sparse graphs. The method involves mapping the original graphs to their line graphs. We show that graphs satisfying a particular property, which we call the square-degree property are sparse, but give rise to dense line graphs. This enables the use of results on graph limits of dense graphs to derive convergence. In particular, star graphs satisfy the square-degree property resulting in dense line graphs and non-zero graphons of line graphs. We demonstrate empirically that we can distinguish different numbers of stars (which are sparse) by the graphons of their corresponding line graphs. Whereas in the original graphs, the different number of stars all converge to the zero graphon due to sparsity. Similarly, superlinear preferential attachment graphs give rise to dense line graphs almost surely. In contrast, dense graphs, including Erdos-Renyi graphs make the line graphs sparse, resulting in the zero graphon.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 49 references that are in the map. First posted on arXiv 3 Sep 2024.
Builds on
9 of 49 references.
References · 9
- 2020Graphon Neural Networks and the Transferability of Graph Neural Networkscited by 117
- 2023A graphon-signal analysis of graph neural networkscited by 29
- 2013Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structurescited by 22
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- 2015The Class of Random Graphs Arising from Exchangeable Random Measurescited by 12
- 2021Asymptotics of Network Embeddings Learned via Subsamplingcited by 10
- 2016Edge-exchangeable graphs and sparsitycited by 7
- 2021Graphon based Clustering and Testing of Networks: Algorithms and Theorycited by 6
- 2019Sampling random graph homomorphisms and applications to network data analysiscited by 4
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Citing papers per year: 2026: 1.
1 paper.