Saved in:
Bibliographic Details
Main Authors: Beemer, Allison, Bolkema, Jessalyn
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.16583
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866915404809502720
author Beemer, Allison
Bolkema, Jessalyn
author_facet Beemer, Allison
Bolkema, Jessalyn
contents Detection of communities in a graph entails identifying clusters of densely connected vertices; the area has a variety of important applications and a rich literature. The problem has previously been situated in the realm of error correcting codes by viewing a graph as a noisy version of the assumed underlying communities. In this paper, we introduce an encoding of community structure along with the resulting code's parameters. We then present a novel algorithm, SASH, to decode to estimated communities given an observed dataset. We demonstrate the performance of SASH via simulations on an assortative planted partition model and on the Zachary's Karate Club dataset.
format Preprint
id arxiv_https___arxiv_org_abs_2507_16583
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SASH: Decoding Community Structure in Graphs
Beemer, Allison
Bolkema, Jessalyn
Social and Information Networks
Information Theory
Combinatorics
Detection of communities in a graph entails identifying clusters of densely connected vertices; the area has a variety of important applications and a rich literature. The problem has previously been situated in the realm of error correcting codes by viewing a graph as a noisy version of the assumed underlying communities. In this paper, we introduce an encoding of community structure along with the resulting code's parameters. We then present a novel algorithm, SASH, to decode to estimated communities given an observed dataset. We demonstrate the performance of SASH via simulations on an assortative planted partition model and on the Zachary's Karate Club dataset.
title SASH: Decoding Community Structure in Graphs
topic Social and Information Networks
Information Theory
Combinatorics
url https://arxiv.org/abs/2507.16583