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Community evolution in patent networks: technological change and network dynamics.

Yuan Gao¹, Zhen Zhu², Raja Kali³

¹1IMT School for Advanced Studies Lucca, Piazza San Francesco 19, Lucca, 55100 Italy.

Applied Network Science

|March 7, 2019

Summary

This study introduces a novel network analysis method for patent data, enhancing the understanding of technological innovation. The approach offers a more stable and dynamic view compared to traditional metrics.

Keywords:

Louvain community detection method Overlapping community mapping Patent data Technological change Temporal networks

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Area of Science:

Bibliometrics
Network Science
Innovation Studies

Background:

Conventional patent analysis indicators may be insufficient for understanding technological change.
Existing patent classification systems can lead to inaccuracies and misclassification of technologies.
Prior work by Gao et al. (2017) proposed analyzing patent classes as network communities.

Purpose of the Study:

To improve the stability and consistency of patent network community detection.
To develop a method for identifying central nodes and tracking community evolution over time.
To offer a dynamic and more stable approach to analyzing patent data for innovation insights.

Main Methods:

Adoption of the stabilized Louvain method for network community detection.
Integration of an overlapping community mapping algorithm.
Development of a novel method for identifying central nodes based on temporal network evolution.

Main Results:

The stabilized Louvain method enhances consistency and stability in community detection.
The new method effectively identifies central nodes and tracks dynamic changes in technology communities.
A case study using German patent data validates the method's applicability and effectiveness.

Conclusions:

The proposed network analysis method provides a more stable and dynamic perspective on technological innovation than conventional metrics.
This approach offers a valuable heuristic for analyzing patent data and understanding shifts in technology landscapes.
The method demonstrates improved accuracy and stability in identifying technology communities and their evolution.