Table of Links Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 3 Data 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.3 Clustering 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.3 Limitations and Future Works 6 Conclusion and References Appendix 2.3 Technology Convergence Models This section explores graph-based and forecasting models used to identify and predict clusters of converging technologies from among other common technologies. 2.3.1 Graph-based Models Graph-based strategies provide an alternative, allowing for an examination of technological convergence from a macro perspective, rather than relying solely on isolated pairwise analysis [8, 11, 20–23]. Cluster analyses and hypergraphs facilitate this process, aiding in the identification and prediction of converging technology clusters [8, 11, 20, 22, 23]. This approach recognizes that technological convergence generally occurs within groups, highlighting the limitations of pairwise analyses in a field characterized by interconnected networks [22, 24]. However, the broader perspective of graph-based methods may obscure subtle interactions at the micro-level, potentially oversimplifying or misrepresenting complex relationships and leading to information loss or biased interpretations. 2.3.2 Forecasting Models Forecasting models for technological convergence leverage a diverse range of algorithms and methodologies. These models incorporate graph-based clustering algorithms such as Spectral Louvain Modularity (SLM) and the Louvain method, Design Structure Matrix (DSM) tools, random forests, and topological clustering to analyze the connections between technological evolutions and emergences [8, 12, 23, 25–27]. In addition, predictive models utilize AutoRegressive Integrated Moving Average (ARIMA), neural networks, and exponential smoothing techniques to anticipate technological trends [12, 25, 26, 28]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 2.3 Technology Convergence Models 3 Data 3 Data 4 Method and 4.1 Proximity Indices 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.2 Interpolation and Fitting Data 4.3 Clustering 4.3 Clustering 4.4 Forecasting 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.2 Case Study 5.3 Limitations and Future Works 5.3 Limitations and Future Works 6 Conclusion and References 6 Conclusion and References Appendix Appendix 2.3 Technology Convergence Models This section explores graph-based and forecasting models used to identify and predict clusters of converging technologies from among other common technologies. 2.3.1 Graph-based Models 2.3.1 Graph-based Models Graph-based strategies provide an alternative, allowing for an examination of technological convergence from a macro perspective, rather than relying solely on isolated pairwise analysis [8, 11, 20–23]. Cluster analyses and hypergraphs facilitate this process, aiding in the identification and prediction of converging technology clusters [8, 11, 20, 22, 23]. This approach recognizes that technological convergence generally occurs within groups, highlighting the limitations of pairwise analyses in a field characterized by interconnected networks [22, 24]. However, the broader perspective of graph-based methods may obscure subtle interactions at the micro-level, potentially oversimplifying or misrepresenting complex relationships and leading to information loss or biased interpretations. 2.3.2 Forecasting Models 2.3.2 Forecasting Models Forecasting models for technological convergence leverage a diverse range of algorithms and methodologies. These models incorporate graph-based clustering algorithms such as Spectral Louvain Modularity (SLM) and the Louvain method, Design Structure Matrix (DSM) tools, random forests, and topological clustering to analyze the connections between technological evolutions and emergences [8, 12, 23, 25–27]. In addition, predictive models utilize AutoRegressive Integrated Moving Average (ARIMA), neural networks, and exponential smoothing techniques to anticipate technological trends [12, 25, 26, 28]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Authors: Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland.
