A Map Equation with Metadata: Varying the Role of Attributes in Community Detection

TL;DR

This paper introduces a tunable extension to the map equation for community detection that incorporates metadata, allowing control over the influence of attributes like gender or grade in identifying communities.

Contribution

It proposes a new parameterized method to balance structural connectivity and metadata, improving community detection flexibility and effectiveness.

Findings

Can overcome structural detectability limits with well-aligned metadata

Achieves higher mutual information with metadata on real networks

Provides a 'zoom' control for community focus based on attributes

Abstract

Much of the community detection literature studies structural communities, communities defined solely by the connectivity patterns of the network. Often, networks contain additional metadata which can inform community detection such as the grade and gender of students in a high school social network. In this work, we introduce a tuning parameter to the content map equation that allows users of the Infomap community detection algorithm to control the metadata's relative importance for identifying network structure. On synthetic networks, we show that our algorithm can overcome the structural detectability limit when the metadata is well-aligned with community structure. On real-world networks, we show how our algorithm can achieve greater mutual information with the metadata at a cost in the traditional map equation. Our tuning parameter, like the focusing knob of a microscope, allows users to "zoom in" and "zoom out" on communities with varying levels of focus on the metadata.