βPix의 GEF 활성에 대한 생화학적 연구

Thesis (master`s)--서울대학교 대학원 :생명과학부,2001.Maste

통계 물리학의 관점에서 바라본 계산적 사회 연결망 분석

학위논문(박사)--서울대학교 대학원 :자연과학대학 물리학과,2013. 8. 강병남.In this thesis, we study three different systems in relations with social phenomena. Two of them are empirical studies on social networks and the other one is a model of self-organized criticality. In Chapter 1, we introduce recent attention of physicists on social phenomena. In Chapter 2, we study time evolution of coauthorship networks. Coauthorship network is a kind of social network of scientific communities. The study contains the first observation and analysis of early stage of social network evolution. In Chapter 3, we study bipartite network of legislators and their followers in Twitter. It reveals a gap between the real ideological positions of legislators and perceived ones by Twitter users. It also shows political biases of Twitter users who follow legislators. The study is an example of the methods based on objective behavior of individuals that overcomes shortcomings of survey based methods. In Chapter 4, we study the Drossel-Schwabl forest fire model on scale-free networks. The model can be a simple model of social crisis such as financial crisis. It was proposed as a non-conservative model of self-organized criticality but has been shown that its scaling behavior is suspicious. However, the model running on scale-free networks shows unexpected scaling behavior.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2. Complete trails of evolution of coauthorship networks . . . . 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Empirical data and construction of networks . . . . . . . . . 9 2.3 Large scale evolution and structural properties . . . . . . . . 10 2.4 Fractal structure of the giant component . . . . . . . . . . . . 19 2.5 Microscopic link dynamics . . . . . . . . . . . . . . . . . . 20 2.6 Effect of link degradation . . . . . . . . . . . . . . . . . . . 25 2.7 Network evolution model . . . . . . . . . . . . . . . . . . . 29 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3. Ideological landscapes on bipartite network in social media . 39 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Classical MDS . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.4 MDS results . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.5 Twitter vs. roll call . . . . . . . . . . . . . . . . . . . . . . . 51 iii3.6 Ideological spectrum of the Twitter users . . . . . . . . . . . 53 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4. Forest fire model on SF networks . . . . . . . . . . . . . . . . 57 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Definition of the model . . . . . . . . . . . . . . . . . . . . 60 4.3 Two distinct regions of fire size distribution . . . . . . . . . 61 4.4 Finite size scaling for large fires . . . . . . . . . . . . . . . . 62 4.5 Scaling relations . . . . . . . . . . . . . . . . . . . . . . . . 68 4.6 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . 75 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Appendix A. Branching process approach for Boolean bipartite networks of metabolic reactions . . . . . . . . . . . . . . . . 79 A.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 79 A.2 Boolean cascade model . . . . . . . . . . . . . . . . . . . . 80 A.3 Branching process approach . . . . . . . . . . . . . . . . . . 81 A.4 Power-law degree distributions . . . . . . . . . . . . . . . . 85 A.5 Numerical simulations . . . . . . . . . . . . . . . . . . . . . 86 A.6 Real-world metabolic networks . . . . . . . . . . . . . . . . 88 A.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 ivAbstract in Korean . . . . . . . . . . . . . . . . . . . . . . . . . . 99Docto

βPix-d의 세포내 위치 조절 및 MAP1 LC1과의 상호작용을 통한 microtubule 안정화 과정에서의 βPix 기능에 대한 연구

Thesis(doctor`s) --서울대학교 대학원 :생명과학부,2006.Docto