A two-layer team-assembly model for invention networks

TL;DR

This paper introduces a novel two-layer model that captures the interdependent inventor and company collaboration networks, accurately replicating real-world patent data using only local information, unlike previous models.

Contribution

It presents the first model to replicate interdependent inventor and company networks using solely local information, improving realism and data fit over existing models.

Findings

The model accurately reproduces degree distributions of real patent networks.

It outperforms existing models in replicating empirical network structures.

Uses only local information, enhancing practical applicability.

Abstract

Companies are exposed to rigid competition, so they seek how best to improve the capabilities of their innovations. One strategy is to collaborate with other companies in order to speed up their own innovations. Such inter-company collaborations are conducted by inventors belonging to the companies. At the same time, the inventors also seem to be affected by past collaborations between companies. Therefore, interdependency of two networks, namely inventor and company networks, exists. This paper discusses a model that replicates two-layer networks extracted from patent data of Japan and the United States in terms of degree distributions. The model replicates two-layer networks with the interdependency. Moreover it is the only model that uses local information, while other models have to use overall information, which is unrealistic. In addition, the proposed model replicates empirical data better than other models.