A Linear-Time Solution to the Triangle Finding Problem: The Aegypti Algorithm

This paper introduces an efficient algorithm for detecting triangles in undirected graphs with a time complexity of O(n + m), where n is the number of vertices and m is the number of edges. By avoiding costly matrix multiplications, the method is particularly effective for sparse graphs. We provide a rigorous proof of correctness, ensuring all triangles are identified without omissions or duplicates, and validate the algorithm's linear-time performance. This advancement enhances sparse graph analysis, enabling faster triangle detection, clustering coefficient computation, and community detection. Applications include social network analysis, bioinformatics, and recommendation systems, making it a practical tool for studying large-scale networks and their properties.

Authors

Publication Details

Published
2025-08-12
DOI
https://doi.org/10.36227/techrxiv.174345120.08185077/v4
Primary Topic
Mathematics and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Linear-Time Solution to the Triangle Finding Problem: The Aegypti Algorithm

Frank Vega
Mathematics and Applications
article

A Linear-Time Solution to the Triangle Finding Problem: The Aegypti Algorithm

Frank Vega
article en

Abstract

This paper introduces an efficient algorithm for detecting triangles in undirected graphs with a time complexity of O(n + m), where n is the number of vertices and m is the number of edges. By avoiding costly matrix multiplications, the method is particularly effective for sparse graphs. We provide a rigorous proof of correctness, ensuring all triangles are identified without omissions or duplicates, and validate the algorithm's linear-time performance. This advancement enhances sparse graph analysis, enabling faster triangle detection, clustering coefficient computation, and community detection. Applications include social network analysis, bioinformatics, and recommendation systems, making it a practical tool for studying large-scale networks and their properties.

Openalex Percentile: Top 74%
Mathematics and Applications
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.