Community Detection with the Map Equation and Infomap: Theory and Applications

Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand—and which variant to choose—we review the map equation’s theoretical framework and guide users in applying the map equation to various research problems.

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Publication Details

Journal
ACM Computing Surveys
Published
2025-12-06
DOI
https://doi.org/10.1145/3779648
Citations
2
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.65
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article

Community Detection with the Map Equation and Infomap: Theory and Applications

Christopher Blöcker, Magnus Neuman, Daniel Edler, Anton Holmgren et al.
2 citations
ACM Computing Surveys
Complex Network Analysis Techniques
2.65
article

Community Detection with the Map Equation and Infomap: Theory and Applications

Christopher Blöcker, Magnus Neuman, Daniel Edler, Anton Holmgren, Martin Rosvall, Jelena Smiljanić
article en
2 citations

Abstract

Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand—and which variant to choose—we review the map equation’s theoretical framework and guide users in applying the map equation to various research problems.

ACM Computing SurveysVol. 58(7)
ZHAW Zurich University of Applied Sciences (CH), University of Würzburg (DE), University of Belgrade (RS), University of Gothenburg (SE), Umeå University (SE)
Openalex Percentile: Top 8%
Complex Network Analysis Techniques
2.65
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Community Detection with the Map Equation and Infomap: Theory and Applications — Christopher Blöcker, Magnus Neuman, et al. · ACM Computing Surveys (2025) | TGRS Research Map | TGRS