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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
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.
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.
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