Enumeration of interval graphs and d-representable complexes

Abstract For each fixed d ≥ 1, we obtain asymptotic estimates for the number of d -representable simplicial complexes on n vertices as a function of n . The case d = 1 corresponds to counting interval graphs, and we obtain new results in this well-studied case as well. Our results imply that the d -representable complexes comprise a vanishingly small fraction of d -collapsible complexes.

Authors

Institutions

Publication Details

Journal
Israel Journal of Mathematics
Published
2025-09-04
DOI
https://doi.org/10.1007/s11856-025-2814-2
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enumeration of interval graphs and d-representable complexes

Boris Bukh, R. Amzi Jeffs
Israel Journal of Mathematics
Topological and Geometric Data Analysis
article

Enumeration of interval graphs and d-representable complexes

Boris Bukh, R. Amzi Jeffs
article en

Abstract

Abstract For each fixed d ≥ 1, we obtain asymptotic estimates for the number of d -representable simplicial complexes on n vertices as a function of n . The case d = 1 corresponds to counting interval graphs, and we obtain new results in this well-studied case as well. Our results imply that the d -representable complexes comprise a vanishingly small fraction of d -collapsible complexes.

Israel Journal of Mathematics
Carnegie Mellon University (US)
National Science Foundation
Openalex Percentile: Top 75%
Topological and Geometric Data Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.