Pith. sign in

REVIEW 1 cited by

A limit theorem for the $1$st Betti number of layer-$1$ subgraphs in random graphs

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1911.00585 v1 pith:KHI6GUWI submitted 2019-11-01 math.CO cs.DMmath.ATmath.PR

classification math.COcs.DMmath.ATmath.PR
keywords graphslayer-subgraphsbettinumberrandomgraphlimit
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We initiate the study of local topology of random graphs. The high level goal is to characterize local "motifs" in graphs. In this paper, we consider what we call the layer-$r$ subgraphs for an input graph $G = (V,E)$: Specifically, the layer-$r$ subgraph at vertex $u \in V$, denoted by $G_{u; r}$, is the induced subgraph of $G$ over vertex set $\Delta_{u}^{r}:= \left\{v \in V: d_G(u,v) = r \right\}$, where $d_G$ is shortest-path distance in $G$. Viewing a graph as a 1-dimensional simplicial complex, we then aim to study the $1$st Betti number of such subgraphs. Our main result is that the $1$st Betti number of layer-$1$ subgraphs in Erd\H{o}s--R\'enyi random graphs $G(n,p)$ satisfies a central limit theorem.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Graph Representation Learning with Localized Topological Features

    cs.LG 2025-01 conditional novelty 5.0 of 10

    Localized persistent homology features can make graph neural networks more expressive and slightly more accurate, but the state-of-the-art claim is not uniformly supported.

Pith tools