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Detecting network communities beyond assortativity-related attributes.

Xin Liu¹, Tsuyoshi Murata², Ken Wakita³

¹Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8552 Japan and CREST, Japan Science and Technology Agency, K's Gobancho, 7 Gobancho, Chiyoda, Tokyo, 102-0076 Japan and Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, 430070 China.

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|August 15, 2014

Summary

This summary is machine-generated.

This study introduces distance modularity to uncover hidden community structures in networks by accounting for attribute similarity (assortativity). It helps reveal communities not explained by specific attributes.

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Area of Science:

Network Science
Complex Systems
Data Mining

Background:

Assortativity describes link formation between nodes with similar attributes in networks.
Existing methods struggle to isolate community structures from attribute-driven topology effects.
Quantifying the influence of specific attributes (ρ) on network topology is challenging.

Purpose of the Study:

To develop a flexible method for detecting community structures independent of specific assortativity-related attributes (ρ).
To enable the simulation of attribute effects on network topology to reveal underlying community patterns.
To provide a tool for probing the influence of assortativity and identifying hidden communities.

Main Methods:

Proposed a novel 'distance modularity' measure.
Introduced the flexibility to use custom functions for simulating the effect of assortativity (ρ).
Evaluated the method on synthetic benchmarks and real-world network datasets.

Main Results:

Distance modularity effectively simulates the impact of specific attributes on network topology.
The method successfully revealed hidden community structures not attributable to the simulated attributes.
Demonstrated effectiveness on both synthetic and real-world network data.

Conclusions:

Distance modularity offers a powerful approach to disentangle attribute-driven network formation from inherent community structures.
This method enhances the ability to detect subtle community patterns by controlling for known attribute biases.
The flexibility of distance modularity allows for broader applications in network analysis across various domains.