Evaluating Local Community Methods in Networks

TL;DR

This paper introduces a new benchmarking procedure for local community detection methods in networks, enabling accurate comparison of different algorithms using synthetic benchmarks tailored to local properties.

Contribution

It presents a novel, unambiguous benchmarking framework and synthetic networks specifically designed for evaluating local community detection algorithms.

Findings

Benchmarking procedure effectively compares local community methods.

Synthetic networks test properties unique to local methods.

Application demonstrates the framework's utility across algorithms.

Abstract

We present a new benchmarking procedure that is unambiguous and specific to local community-finding methods, allowing one to compare the accuracy of various methods. We apply this to new and existing algorithms. A simple class of synthetic benchmark networks is also developed, capable of testing properties specific to these local methods.