臺灣博碩士論文加值系統

ABSTRACT
Recent years, social network has gained much popularity because of the opportunities it gives entities to connect to each other in an easy manner, and to exchange and share various kind of information through this network. Besides, the social network analysis provides a set of methods for analyzing the structures of all entities as well as a variety of theories explaining the patterns observed in this network structures. The study of these structures uses social network analysis to identify the local and global patterns, locate influential entities and key players, and examine network characteristics.
In this study, we proposed a method to analyze the patent influence of well-known companies in terms of patent-infringement relationship in pharmaceutical industry which is one of the most successful industrial sectors in the world. For this purpose, the network graph of patent-infringement is constructed using the data from patent-infringement lawsuits cases between pharmaceutical companies and the influential level of these companies are analyzed by computing their network centrality in the network graph. In particular, the proposed study applies social network analysis to make performance prediction via three major tasks, including identifying roles of companies, classification and forecasting tasks. The first task of identifying roles of companies analyses the influence of companies by computing network centrality in the constructed graph of patent-infringement lawsuit of well-known companies in the pharmaceutical industry. The second task of classification uses corporation and financial performance features to do classification with the implementation of the Naïve Bayes algorithm. The third task of forecasting verifies the impact of centrality measures from the patent infringement network on business indicators such as global market value and gross profit via regression analysis. The results from these major tasks are discussed together for a more understanding of performance prediction behind patent infringement relationship between pharmaceutical companies. This study could be a beneficial contribution for managers to make efficient prediction and do strategic planning for a successful business.

Recent years, social network has gained much popularity because of the opportunities it gives entities to connect to each other in an easy manner, and to exchange and share various kind of information through this network. Besides, the social network analysis provides a set of methods for analyzing the structures of all entities as well as a variety of theories explaining the patterns observed in this network structures. The study of these structures uses social network analysis to identify the local and global patterns, locate influential entities and key players, and examine network characteristics.
In this study, we proposed a method to analyze the patent influence of well-known companies in terms of patent-infringement relationship in pharmaceutical industry which is one of the most successful industrial sectors in the world. For this purpose, the network graph of patent-infringement is constructed using the data from patent-infringement lawsuits cases between pharmaceutical companies and the influential level of these companies are analyzed by computing their network centrality in the network graph. In particular, the proposed study applies social network analysis to make performance prediction via three major tasks, including identifying roles of companies, classification and forecasting tasks. The first task of identifying roles of companies analyses the influence of companies by computing network centrality in the constructed graph of patent-infringement lawsuit of well-known companies in the pharmaceutical industry. The second task of classification uses corporation and financial performance features to do classification with the implementation of the Naïve Bayes algorithm. The third task of forecasting verifies the impact of centrality measures from the patent infringement network on business indicators such as global market value and gross profit via regression analysis. The results from these major tasks are discussed together for a more understanding of performance prediction behind patent infringement relationship between pharmaceutical companies. This study could be a beneficial contribution for managers to make efficient prediction and do strategic planning for a successful business.

Table of Contents
ABSTRACT i
Table of Contents iii
List of Figures v
List of Table vii
CHAPTER 1 INTRODUCTION 1
1.1 Background and Motivation 1
1.2 Research Problem 3
1.3 Research Objectives 5
1.4 Research Procedure and Thesis Framework 6
CHAPTER 2 LITERATURE REVIEW 9
2.1 Data mining 9
2.2 Social Network Analysis 13
2.2.1 Social Network 13
2.2.2 Social Network Analysis (SNA) 16
2.3 Social Network Analysis of Patent Infringement Lawsuit Analysis in Pharmaceutical Industry 17
2.4 Related Work 19
CHAPTER 3 REARCH METHODOLOGY 22
3.1 Research Framework 22
3.2 Data Collection 23
3.3 Social Network Analysis 24
3.4 Task 1 – Identifying Roles of Companies 29
3.5 Task 2 – Classification 33
3.6 Task 3 – Forcasting 37
3.6 Summary 40
CHAPTER 4 IMPLEMENTATION AND EXPERIMENT RESULTS 41
4.1 Data Collection 41
4.1.1 Data of Patent Infringement Lawsuit 41
4.1.2 Financial Data 42
4.2 Social Network Analysis Results 44
4.2.1 Network Centrality Measures 44
4.2.2 Visualization of Patent Infringement Lawsuit Network 45
4.3 Identifying Roles of Companies Task 49
4.4 Classification Task 53
4.5 Forecasting Task 56
4.6 Summary and Discussion 66
CHAPTER 5 CONCLUSION and FUTURE WORK 69
5.1 Conclusion 69
5.2 Future Work 72
Reference 75
APPENDIX A – PATENT INFRINGEMENT LASWSUIT CASES 80
APPENDIX B – MATLAB PROGRAMING CODE 88

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