# Community Detection with Colored Edges

## TL;DR

This paper establishes a precise threshold for community detection in graphs with multiple edge types, showing when maximum likelihood methods can successfully identify communities based on edge distribution parameters.

## Contribution

It provides a sharp theoretical limit for community detection with colored edges, extending previous models to multiple edge types and characterizing detectability via Hellinger distance.

## Key findings

- Detection is possible if sum of squared differences exceeds 2
- Detection fails with high probability if sum is below 2
- Threshold depends on edge distribution parameters _i, _i

## Abstract

In this paper, we prove a sharp limit on the community detection problem with colored edges. We assume two equal-sized communities and there are $m$ different types of edges. If two vertices are in the same community, the distribution of edges follows $p_i=\alpha_i\log{n}/n$ for $1\leq i \leq m$, otherwise the distribution of edges is $q_i=\beta_i\log{n}/n$ for $1\leq i \leq m$, where $\alpha_i$ and $\beta_i$ are positive constants and $n$ is the total number of vertices. Under these assumptions, a fundamental limit on community detection is characterized using the Hellinger distance between the two distributions. If $\sum_{i=1}^{m} {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2 >2$, then the community detection via maximum likelihood (ML) estimator is possible with high probability. If $\sum_{i=1}^m {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2 < 2$, the probability that the ML estimator fails to detect the communities does not go to zero.