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Related Experiment Video

Updated: Dec 27, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Efficient community detection algorithm based on higher-order structures in complex networks.

Jinyu Huang¹, Yani Hou¹, Yuansong Li¹

¹College of Computer Science, Sichuan University of Science and Engineering, Zigong 643000, People's Republic of China.

Chaos (Woodbury, N.Y.)

|March 2, 2020

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for community detection in complex networks using higher-order structures like motifs. The method effectively identifies communities based on various motif types and proves robust in real-world and simulated network analyses.

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Area of Science:

Network Science
Graph Theory
Data Mining

Background:

Community detection is crucial for understanding complex networks.
Traditional methods often overlook higher-order connectivity patterns.
Motifs represent fundamental building blocks of network structure.

Purpose of the Study:

To develop an efficient algorithm for community detection based on higher-order structures (motifs).
To extend the algorithm for detecting communities using signed, colored, weighted, or multiple motifs.
To introduce higher-order structure-based stochastic block models for network generation.

Main Methods:

Developed an efficient community detection algorithm leveraging higher-order connectivity patterns.
Incorporated flexibility to detect communities based on various motif types (signed, colored, weighted, multiple).
Introduced stochastic block models grounded in higher-order structures.

Main Results:

The proposed algorithm effectively identifies communities based on higher-order connectivity patterns.
Demonstrated effectiveness on both real-world networks and computer-generated graphs.
The algorithm's performance validates its capability in capturing complex community structures.

Conclusions:

The developed algorithm provides an effective solution for community detection in complex networks.
Higher-order connectivity patterns, particularly motifs, are valuable for accurate community identification.
The approach offers a robust framework for analyzing network structures with diverse motif configurations.