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DANI: fast diffusion aware network inference with preserving topological structure property.

Maryam Ramezani¹, Aryan Ahadinia¹, Erfan Farhadi¹

¹Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.

Scientific Reports

|December 27, 2024

Summary

This summary is machine-generated.

We developed DANI, a new method for inferring social network structures from information cascades. DANI accurately reconstructs networks while preserving key topological properties, outperforming existing methods.

Keywords:

Diffusion Information Network Inference Network Science Social Networks Structure Preserving Topological Structure

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Area of Science:

Network Science
Data Mining
Computational Social Science

Background:

Inferring social network structure from information propagation is crucial.
Existing methods often prioritize link accuracy over preserving network topology.

Purpose of the Study:

To propose a novel method, DANI, for inferring social network structure.
To ensure the preservation of essential topological properties during network inference.

Main Methods:

DANI utilizes a Markov transition matrix derived from time series cascade analysis.
It incorporates node-node similarity in cascade behavior from a structural viewpoint.
The method offers linear time complexity and a scalable MapReduce version.

Main Results:

DANI demonstrated higher accuracy and reduced runtime compared to established network inference techniques.
The method successfully preserved key structural properties like modularity, degree distribution, and clustering coefficients.
Experiments were conducted on both real-world and synthetic network data.

Conclusions:

DANI provides an effective approach for social network inference that balances accuracy with structural integrity.
The method's efficiency and scalability make it suitable for large-scale network analysis.
Preserving topological properties is vital for understanding network dynamics.