From 04591ef39597ddeb23eda711276d6217bde9cebb Mon Sep 17 00:00:00 2001
From: calcudimen <32936027+calcudimen@users.noreply.github.com>
Date: Tue, 17 Sep 2024 18:21:52 -0400
Subject: [PATCH] Update the abstract

Updating the abstract to match the abstract in the paper.
- from author Chenghao Guo
---
 _posts/2024-06-30-bresler24a.md | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/_posts/2024-06-30-bresler24a.md b/_posts/2024-06-30-bresler24a.md
index b180580..310187f 100644
--- a/_posts/2024-06-30-bresler24a.md
+++ b/_posts/2024-06-30-bresler24a.md
@@ -1,16 +1,7 @@
 ---
 title: Thresholds for Reconstruction of Random Hypergraphs From Graph Projections
 section: Original Papers
-abstract: 'The graph projection of a hypergraph is a simple graph with the same vertex
-  set and with an edge between each pair of vertices that appear in a hyperedge. We
-  consider the problem of reconstructing a random $d$-uniform hypergraph from its
-  projection. Feasibility of this task depends on $d$ and the density of hyperedges
-  in the random hypergraph. For $d=3$ we precisely determine the threshold, while
-  for $d\geq 4$ we give bounds. All of our feasibility results are obtained by exhibiting
-  an efficient algorithm for reconstructing the original hypergraph, while infeasibility
-  is information-theoretic. Our results also apply to mildly inhomogeneous random
-  hypergrahps, including hypergraph stochastic block models. A consequence of our
-  results is that claims from the 2023 COLT paper gaudio’23 are disproved. '
+abstract: 'The graph projection of a hypergraph is a simple graph with the same vertex set and with an edge between each pair of vertices that appear in a hyperedge. We consider the problem of reconstructing a random d-uniform hypergraph from its projection. Feasibility of this task depends on d and the density of hyperedges in the random hypergraph. For $d =3$ we precisely determine the threshold, while for $d\ge 4$ we give bounds. All of our feasibility results are obtained by exhibiting an efficient algorithm for reconstructing the original hypergraph, while infeasibility is information-theoretic. Our results also apply to mildly inhomogeneous random hypergrahps, including hypergraph stochastic block models (HSBM). A consequence of our results is an optimal HSBM recovery algorithm, improving on Gaudio and Joshi (2023a). '
 layout: inproceedings
 series: Proceedings of Machine Learning Research
 publisher: PMLR