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Network-based stochastic competitive learning approach to disambiguation in collaborative networks.

Thiago Christiano Silva¹, Diego Raphael Amancio

¹Institute of Mathematics and Computer Science, University of São Paulo, P. O. Box 369, São Carlos, São Paulo 13560-970, Brazil. thiagoch@icmc.usp.br

Chaos (Woodbury, N.Y.)

|April 6, 2013

Summary

This study introduces a novel unsupervised method for name disambiguation in collaborative networks using particle competition. The technique accurately identifies distinct individuals within noisy datasets, improving pattern discovery in complex systems.

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Area of Science:

Complex Systems Science
Network Science
Data Mining

Background:

Complex network analysis reveals patterns but is hindered by data noise, such as homonymous individuals.
Accurate data is crucial for validating patterns in social and collaborative networks.

Purpose of the Study:

To address the challenge of name disambiguation in collaborative networks.
To develop an unsupervised technique for improving the accuracy of pattern discovery in noisy network data.

Main Methods:

An unsupervised particle competition mechanism within a networked environment is proposed.
Particles utilize a mixed strategy of random and preferential walking on the network.
The method leverages network topology and particle domination levels for disambiguation.

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Main Results:

The particle competition model effectively detects clusters and disambiguates names.
Simulations on arXiv and other databases demonstrate superior accuracy compared to traditional clustering.
The approach successfully handles noise from homonymous individuals in collaborative networks.

Conclusions:

The proposed particle competition model offers a robust solution for name disambiguation.
This method enhances the reliability of pattern discovery in complex systems.
Network representation and particle dynamics are key to improving learning processes.