Prediction of emerging technologies based on analysis of the US patent citation network

Abstract The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, an

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Prediction of emerging technologies based on analysis of the US patent citation network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action. © 2012 Akadémiai Kiadó, Budapest, Hungary

Wired into Each Other: Network Dynamics of Adolescents in Hungarian Secondary Schools: 2010-2013

The project “Wired into each other” is a three-year longitudinal social network study conducted by the Research Center for Educational and Network Studies (RECENS) of Corvinus University of Budapest and the Hungarian Academy of Sciences. The study involved the collection of a unique, large-scale survey dataset about the evolution of interpersonal relations and various individual behaviours and attitudes in more than 40 student communities from Hungary between 2010 and 2013. The project aimed at a) developing novel measures of informal social networks among students and b) gaining new insight into the social processes shaping adolescent communities. In scope of this, the RECENS team developed a multi-item network questionnaire about peer relations in more than 30 different aspects, including contact, affection, behavioural perceptions, role and status attributions, and bullying. Using this measurement tool, the team collected data of unprecedented depth about the multidimensional nature of social processes in school communities. The dataset allows researchers to study the social mechanisms behind status competition, group formation, ethnic integration (with focus on the Roma minority group), bullying and victimization, school performance, substance use, and other phenomena.The project “Wired into each other” is a three-year longitudinal social network study conducted by the Research Center for Educational and Network Studies (RECENS) of Corvinus University of Budapest and the Hungarian Academy of Sciences. The study involved the collection of a unique, large-scale survey dataset about the evolution of interpersonal relations and various individual behaviours and attitudes in more than 40 student communities from Hungary between 2010 and 2013. The project aimed at a) developing novel measures of informal social networks among students and b) gaining new insight into the social processes shaping adolescent communities. In scope of this, the RECENS team developed a multi-item network questionnaire about peer relations in more than 30 different aspects, including contact, affection, behavioural perceptions, role and status attributions, and bullying. Using this measurement tool, the team collected data of unprecedented depth about the multidimensional nature of social processes in school communities. The dataset allows researchers to study the social mechanisms behind status competition, group formation, ethnic integration (with focus on the Roma minority group), bullying and victimization, school performance, substance use, and other phenomena.</p