Partner Selection in China Interorganizational Patent Cooperation Network Based on Link Prediction Approaches

To enhance competitiveness and protect interest, an increasing number of organizations cooperate on patent applications. Partner selection has attracted much more attention because it directly affects the success of patent cooperation. By collecting some cooperative patents applied for by different categories of organizations in China from 2007 to 2015, an interorganizational patent cooperation network was built for this paper. After analyzing certain basic properties of the network, it was found that the network possessed some typical characteristics of social networks. Moreover, the network could be divided into communities, and three communities were selected to analyze as representative. Furthermore, to explore the partner selection in the patent cooperation network, eight link prediction approaches commonly used in social networks were introduced to run on another interorganizational patent cooperation network built by the patents applied for in 2016. The precision metric results of the eight link prediction approaches show that they are effective in partnership prediction; in particular, the Common Neighbors (CN) index can be effectively applied to the selection of unfamiliar partners for organizations in patent cooperation. Moreover, this paper also verified the trust transitivity based not only on historical cooperation but also on geographical location, and the complementarity of capabilities still plays an important role in partner selection for organizations.

Automated summary

This study analyzed the interorganizational patent cooperation network and found typical characteristics of social networks, dividing the network into communities for analysis. Moreover, in partner selection, the Common Neighbors (CN) index was found to be effective in predicting partnership, indicating its usefulness in selecting unfamiliar partners in patent cooperation.