Concept
Trivially perfect graph
Related publications (59)
A full characterization of invariant embeddability of unimodular planar graphs
When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...
WILEY2023Scalable maximal subgraph mining with backbone-preserving graph convolutions
Karl Aberer, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thành Tâm Nguyên
Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...
ELSEVIER SCIENCE INC2023The micro-world of cographs
Cographs constitute a small point in the atlas of graph classes. However, by zooming in on this point, we discover a complex world, where many parameters jump from finiteness to infinity. In the present paper, we identify several milestones in the world of ...
ELSEVIER2022Graph signal processing tailored for subgraph focus and community structure
Community structure in graph-modeled data appears in a range of disciplines that comprise network science. Its importance relies on the influence it bears on other properties of graphs such as resilience, or prediction of missing connections. Nevertheless, ...
EPFL2022Distributed Graph Learning With Smooth Data Priors
Pascal Frossard, Mireille El Gheche, Isabela Cunha Maia Nobre
Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...
IEEE2022Multilayer Graph Clustering With Optimized Node Embedding
Pascal Frossard, Mireille El Gheche
We are interested in multilayer graph clustering, which aims at dividing the graph nodes into categories or communities. To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fid ...
IEEE2021Representing graphs through data with learning and optimal transport
Graphs offer a simple yet meaningful representation of relationships between data. Thisrepresentation is often used in machine learning algorithms in order to incorporate structuralor geometric information about data. However, it can also be used in an inv ...
EPFL2021Improved Ramsey-type results for comparability graphs
Istvan Tomon, Dániel József Korándi
Several discrete geometry problems are equivalent to estimating the size of the largest homogeneous sets in graphs that happen to be the union of few comparability graphs. An important observation for such results is that if G is an n-vertex graph that is ...
CAMBRIDGE UNIV PRESS2020Scale-dependent measure of network centrality from diffusion dynamics
Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A n ...
AMER PHYSICAL SOC2020Ramsey numbers of ordered graphs
An ordered graph is a pair G = (G,
ELECTRONIC JOURNAL OF COMBINATORICS2020