ftp: //163. 25. 117/nplu/ 資訊網絡分析 Social Network Analysis 1
資訊網絡分析 Information Network Analysis n Textbook n n References n n n Hanneman, R. & Riddle, M. (2005), Introduction to Social Network Methods, University of California, Riverside. Easley, D. & Kleinberg, J. (2010), Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press. Borgatti, S. P. , Everett, M. , & Johnson, J. C. (2013), Analyzing Social Networks, SAGE Publications. Grading n n n Midterm Quiz (15%) Midterm Report (20%) Final Exam. (25%) Final Report (25%) Homeworks, etc. (15%) 2
Attention, Please! Attention 20 min conclusion A wealth of information creates a poverty of attention. Time AACSB Ao. L Learning Goal: 1. Students will grasp the knowledge and understand applications of information technology. 1 -1. Students will grasp the knowledge of information technology. 1 -2. Students will demonstrate an understanding of information technology applications. 3
Business Readings n Alone Together: Why We Expect More from Technology and Less from Each Other n n n Now You See It: How Technology and Brain Science Will Transform Schools and Business for the 21 st Century n n n http: //www. books. com. tw/products/0010695047 The App Generation: How Today’s Youth Navigate Identity, Intimacy, and Imagination in a Digital World n n http: //www. books. com. tw/products/0010631097 It’s Complicated: The Social Lives of Networked Teens n n https: //www. amazon. com/gp/product/014312126 X https: //www. youtube. com/watch? v=v. JG 698 U 2 Mvo Too Big To Know n n http: //www. books. com. tw/products/F 012743341 http: //www. books. com. tw/products/0010740203 http: //www. books. com. tw/products/0010673129 Net Smart: How to Thrive Online n http: //www. books. com. tw/products/0010650145 Cell Phone Technology and the Challenge of Absent Presence Prisoners of the Wired World 4
Popular Science Readings n Six Degrees: The Science of a Connected Age n n Superconnect n n http: //www. books. com. tw/products/F 012645633 Bursts: The Hidden Patterns Behind Everything We Do, from Your E-mail to Bloody Crusades n n http: //www. books. com. tw/products/F 012300027 Everything Is Obvious: Once You Know the Answer n n http: //www. books. com. tw/products/0010488473 Connected n n http: //www. books. com. tw/products/0010246961 http: //www. books. com. tw/products/F 012433404 Social Physics: How Good Ideas Spread ─ The Lessons from a New Science n http: //www. books. com. tw/products/0010657275 5
Advances of Information Technologies 6
7
From Websites to Web Applications (APPs) 8
Usages of Social Media 9
Rhythms of social interaction: Messaging within a massive online network (Golder et al. , 2007) n Data: n n Facebook (www. facebook. com) Result: 10
Golder et al. (2007) (2/2) fat-tail distribution 11
Rhythms of Blogging http: //blog. xuite. net/ 12
Rhythms of PTT https: //www. ptt. cc/index. bbs. html 13
Rhythms of Wikipedia http: //wikipedia. org/ 14
Rhythms of Yahoo!Kimo Auction https: //tw. bid. yahoo. com/ 賣方刊登商品時間 買方得標商品時 間 15
Rhythms of Plurk Good night http: //www. plurk. com/top/ 16
Suggested Readings (ftp: //163. 25. 117/nplu) In the directory: /資訊網絡分析/Readings n 00 Inside Social Network Analysis n 01 Social Media Networks n 02 Rhythms of social interaction Papers Year T 03 542 2010 T 04 台灣部落格空間之網絡變遷分析 2012 T 05 JEB 2013 -009 2013 T 06 JEB 2014 -008 2014 T 07 JEB 2014 -017 2014 T 08 02533839. 2015 T 09 02533839. 2016 T 10 2017. 1328680 2017 17
What is a Network? 18
Introduction n Many real world systems can be described as networks. n Social relationships: n n Technological systems: n n e. g. internet topology, WWW, or mobile networks. Biological systems: n n e. g. interaction in social media, collaboration in academic, entertainment, business area. e. g. regulatory, metabolic, or interaction relationships. And so on and so forth… 19
A Simple Network Node degree: 4 Path length: 2 20
Other Example Networks 21
An example network showing community structure 22
A network of collaborations among scientists (Newman, 2011) 23
Almost perfect polarization in the network (Sampson, 1968) 24
Friendship network of children in a US school (Moody, 2001) 25
Communities of political blogs (Adamic and Glance, 2005) 26
The Internet (Lumeta Corp, 2007) 27
Types of Network Ties 28
Network Theory and Theory of Networks Borgatti, S. P. & Halgin, D. S. (2011). On network theory. Organization Science, 22(5), 1168 -1181. 29
Suggested Readings (ftp: //163. 25. 117/nplu) In the directory: /資訊網絡分析/Readings n 03 Complex networks n 04 On network theory n 05 Network theory. pdf n 06 Communities modules and large-scale structure in Network n 07 Network science Web science and Internet science 30
UCINET: A Tool for Social Network Analysis 31
UCINET Basics n Official Site of UCINET n n http: //www. analytictech. com/ Official UCINET Tutorial n http: //faculty. ucr. edu/~hanneman/nettext/ 32
Install UCINET n Get the software n n Then, n n n https: //sites. google. com/site/ucinetsoftware/home Press next, and next… Finally, the software is installed Registration is required to use UCINET legally 33
The UCINET Environment n Main menu n n n File Data Transform Tools Network n n Visualize n n n Many analysis procedures are here Net. Draw Options Help n n Contents: Introduction Section, DL, and Standard Datasets. Index: search by keywords. 34
A Typical Analysis Procedure n Select a procedure from the Network menu 35
Methods of Collecting Network Data 1. Full network … 2. Snowball Step 1 … 2 1 3 4 … 1 Step 2 Step 3 … … 1 3 3 5 5 4 5 … 36
Methods of Collecting Network Data 4. Ego-centric networks (without alter connections) 3. Ego-centric networks (with alter connections) 2 1 3 3 5 2 1 3 5 4 2 3 3 4 5 5 2 3 4 2 1 4 5 3 3 5 4 2 5 4 5 37
Network Data Structures n Adjacency matrix n n UCINET Adjacency list n Net. Draw 38
Blogroll Networks of Taiwan Blogosphere n Select Top 100 blogs from「部落格觀察」 http: //look. urs. tw (Closed Now…) n n In 2008, 2009, 2010, 2011 Find the blogroll networks of the top 100 blogs 2 1 3 4 n n 5 Net. Draw files are in ftp: //163. 25. 117/nplu/%B 8%EA%B 0 T%BA%F 4%B 5%B 8% A 4%C 0%AAR/Blog%20 Networks/ See the paper “T 05 JEB 2013 -009” in ftp: //163. 25. 117/nplu/%B 8%EA%B 0 T%BA%F 4%B 5%B 8% A 4%C 0%AAR/Readings/ 39
Lab 0: A Short Tour in UCINET n Open UCINET n And open datasets… 40
Other Tools (ftp: //163. 25. 117/nplu) In the directory: /資訊網絡分析/Readings n 08 Pajek n 09 Node. XL n 10 E-Net 41
Network Science: A Multi-discipline Research Field 42
The Invasion of the Physicists n First shot: Small-world networks n n Second shot: Scale-free networks n n Watts, D. J. and Strogatz, S. H. (1998), “Collective dynamics of ‘small-world’ networks, ” Nature, 393, 440 -442. Barabási, A. -L. and Albert, R. (1999), “Emergence of scaling in random networks, ” Science, 286, 509 -512. (Social) network analysis in n Social physics System biology Computational social science n n n with supercomputer with computing cloud The defense of the social scientist n Linton C. Freeman, 2008, “Going the Wrong Way on a One-Way Street: Centrality in Physics and Biology, ” Journal of Social Structure, Vol. 9. 43
Small-world Network n The small-world effect (Milgram, 1967) n n Six degrees of separation in USA Watts-Strogatz model (Watts and Strogatz, 1998) n Rewire links from regular to random networks 44
Path Lengths and Clustering Coefficients General Graph Regular Graph Random Graph Characteristic Path Length Clustering Coefficient n: number of nodes in the network d(i, j): shortest path length between nodes i and j ki: degree of node i K: average node degree of the network ei: number of links between the neighbors of node i 45
Comparison of path lengths and clustering coefficients Link Rewire Probability 46
Some Small-world Networks n Newman et al. (2001) low path length high clustering coefficient 47
Degree Distributions of Networks n Poisson distribution Z = (n - 1) p Power-law distribution fat-tail distribution 48
Scale-free Network n n Power-law degree distributions Scale Free Model (Barabási and Albert, 1999) n Incremental growth: The network is growing continuously by adding new nodes or new connections step by step n Preferential connectivity: Highly connected nodes are more likely to be connected again in the process of incremental growth, also called the rich-get-richer phenomenon; 49
Incremental growth and Preferential connectivity 50
Random vs. Scale-free Networks 51
Some Scale-free Networks 52
Properties of Scale-free Networks n n Small World: A small average path length n Mean shortest node-to-node path n Can reach any nodes in a small number of hops, 5~6 hops Modularity: A large clustering coefficient n How many of a node’s neighbors are connected to each other § Power law degree distribution: Rich get richer n n Robustness: Resilient and have strong resistance to failure on random attacks but vulnerable to targeted attacks Disassortative or Assortative n Biological and technological networks: disassortative n Social networks: assortative 53
Roubstness of Scale-free Networks 54
Assortativity of Scale-free Networks 55
Comparison 56
Suggested Readings (ftp: //163. 25. 117/nplu) In the directory: /資訊網絡分析/Readings n 11 Collective dynamics of 'small-world' networks n 12 Emergence of Scaling in Random Networks n 13 Computational social science n 14 Going the Wrong Way on a One-Way Street n 15 Scale. Free_Scientific Ameri 288, 60 -69 (2003) n 16 The physics of networks n 17 The Invasion of the Physicists 57
Скачать презентацию ftp 163 25 117 nplu 資訊網絡分析 Social Network Analysis
9d3ca184b34cd36048a6aa2154915d97.ppt