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

Abstract

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.

Developing a Conceptual Partner Matching Framework for Digital Green Innovation of Agricultural High-End Equipment Manufacturing System Toward Agriculture 5.0: A Novel Niche Field Model Combined With Fuzzy VIKOR

“…In terms of research methods, the partner selection method mainly focuses on the multi-attribute decision making method (Mousavi and Gitinavard, 2019;Zeng et al, 2019), network correlation analysis method (Chen et al, 2021), SEM method (Hair et al, 2019), VIKOR group decision making method (Wang et al, 2019), mathematical analysis method (Bolandnazar et al, 2020), intuitionistic fuzzy theory (Yang et al, 2021), method set (Venkatesh et al, 2019), and stage method (Ávila et al, 2021). For example, Mousavi and Gitinavard (2019) extended a multi-attribute group decision approach for the selection of outsourcing service activities for information technology under risk.…”

“…Zheng et al used expert evaluation and system analysis methods to conduct a comprehensive evaluation on the selection of partners. Chen et al (2021) summarized that quality and capability are the main indicators for the evaluation of industry-university-research collaborative innovation partners. Su et al (2022) found that core members will preferentially select collaborative members with knowledge stock advantages to form a knowledge supply chain.…”

“…Ávilaey et al took virtual enterprises as the research subject and used different methods to study the partner selection of virtual enterprises. In terms of research methods, the virtual enterprises mainly focus on the multi-attribute decision making method (Mousavi and Gitinavard, 2019;Zeng et al, 2019), network correlation analysis method (Chen et al, 2021), SEM method (Hair et al, 2019), VIKOR group decision making method (Wang et al, 2019), mathematical analysis method (Bolandnazar et al, 2020), intuitionistic fuzzy theory (Yang et al, 2021), method set (Venkatesh et al, 2019), and stage method (Ávila et al, 2021).…”

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

“…In the existing research, a series of approaches have been proposed for studying on partner selection in cooperation networks, which can be mainly classified into two families: location-based collaboration and competency-based collaboration. In the early years, limited by undeveloped communication technology and expensive transportation costs, the geographical locations of cooperators is crucial for the selection of partners [30]. Evolution analysis of cooperation networks is very beneficial for the prediction of potential partners.…”

Spatial Structure Characteristics of Tourist Attraction Cooperation Networks in the Yangtze River Delta Based on Tourism Flow

“…As for research on the cooperation network of tourism attractions, most of the previous studies on the government's policies or measures for the cooperation of tourism attractions are from the perspective of supply [14][15][16][17][18][19][20][21]. The cooperation network of tourist attractions is closely related to tourism flow [22][23][24][25][26][27]. On the demand side, the spatial decisionmaking behavior of tourists influences the form, scope, and structure of the cooperation network between tourist attractions.…”