Eytan Bakshy proves weak ties are useful using network theory and Facebook data. Some excerpts from the article:

Bakshy’s work shares some features with previous communications studies on networks, and it confirms some long-held ideas in sociology. (For instance, the idea that weak ties can be important was first floated in a seminal 1973 study by Mark Granovetter.) It also confirms a few other recent studies questioning the echo chamber, including the economists Matthew Gentzkow and Jesse Shapiro’s look at online news segregation.

In this way, his study is like a clinical trial: There’s a treatment group that’s subjected to a certain stimulus and a control group that is not, and Bakshy calculated the differences between the two. This allows him to draw causal relationships between seeing a link and acting on it: If you see a link and reshare it while some other user does not see the link and does not share it, this means that the Facebook feed was responsible for the sharing.

via Slate Magazine

Lars Backstrom, Eytan Bakshy, Jon Kleinberg, Thomas M. Lento, Itamar Rosenn. ICWSM 2011

Abstract:

An individual’s personal network — their set of social contacts — is a basic object of study in sociology. Studies of personal networks have focused on their size (the number of contacts) and their composition (in terms of categories such as kin and co-workers). Here we propose a new measure for the analysis of personal networks, based on the way in which an individual divides his or her attention across contacts. This allows us to contrast people who focus a large fraction of their interactions on a small set of close friends with people who disperse their attention more widely.

Using data from Facebook, we find that this balance of attention is a relatively stable property of an individual over time, and that it displays interesting variation across both different groups of people and different modes of interaction. In particular, activities based on communication involve a much higher focus of attention than activities based simply on observation, and these two types of modalities also exhibit different forms of variation in interaction patterns both within and across groups. Finally, we contrast the amount of attention paid by individuals to their most frequent contacts with the rate of change in the identities of these contacts, providing a measure of churn for this set.

via Michigan Interactive & Social Computing

Modern social network analysis — the analysis of relational data arising from social systems — is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown.

Although the focus of the package 

has been on social scientific applications, many of the included tools may also be useful for 

via Journal of Statistical Software

libSNA is an open-source library for Social Network Analysis, licensed under the LGPL. This library is under active development by Abe Usher in hopes that it will serve as a catalyst for improving the field of Social Network Analysis. Features Easy to use Python API Flexible data import options Scalable Built-in reports Built-in data export capabilities Open source - easily extended Fast processing time (efficient use of graph algorithms)

1970′s

Mullins, Nicholas. Theories and Theory Groups in Contemporary American Sociology. New York: Harper and Row, 1973. Key chapter pre INSNA on early social network analysis.

Wolfe, Alvin W. 1978. The Rise of Network Thinking in Anthropology. Social Networks 1(1978):53-64.

1980′s

Barry Wellman, “Structural Analysis: From Method and Metaphor to Theory and Substance.” Pp. 19-61 in Social Structures: A Network Approach, edited by Barry Wellman and S.D. Berkowitz. Cambridge: Cambridge University Press, 1988.

Winship, C. (1988). Thoughts about roles and relations: An old document revisited. Social Networks, 10, 209-231. (insights into the early development of blockmodelling)

1990′s

John Scott (1991) – Social Network analysis: A handbook (2002) – chapter 2

Freeman, Linton C. (1992). Social Networks and the Structure Experiment. In L. C. Freeman, D. R. White & A. K. Romney (Eds.), Research Methods in Social Network Analysis (pp. 11-40). New Brunswick, NJ: Transaction Publishers.

Hummon, N. and K. Carley. 1993. Social networks as normal science. Social Networks 15:71-106

Wasserman and Faust (1994). Social Network Analysis – Methods and Applications. Cambridge University Press

J.C. Johnson. “Anthropological Contributions to the Study of Social Networks: A Review.” In (S. Wasserman and J. Galaskiowicz, eds.) Advances in Social Network Analysis: Research in the Social and Behavioral Sciences. Sage: Newbury Park. 1994.

Linton Freeman and Barry Wellman. “A Note on the Ancestral Toronto Home of Social Network Analysis.” Connections 18 (November, 1996): 15-19.

Frank, K. A. (1998). “The Social Context of Schooling: Quantitative Methods” Review of Research in Education, Vol, 23, chapter 5, pages 171-216.

2000′s

Barry Wellman, “Networking Network Analysts: How INSNA (the International Network for Social Network Analysis) Came to Be.” Connections 23, 1 (Summer, 2000): 20-31

Azarian, Reza. The General Sociology of Harrison White. Stockholm: Stockholm University, 2003.

Linton C. Freeman (2004) “The Development of Social Network Analysis: A Study in the Sociology of Science” –http://www.amazon.com/Development-Social-Network-Analysis-Sociology/dp/1594577145/ref=pd_sim_b_5

C. Reza Azarian – The General Sociology of Harrison C. White: Chaos and Order in Networks Palgrave Macmillan (April 13, 2006) http://www.amazon.com/General-Sociology-Harrison-C-White/dp/1403944342

2010′s

“The Network Revolution” chapter in Rainie-Wellman Networked: The New Social Operating System.  MIT Press, 2011.

Charles Kadushin : Making Connections: Network Theory, Concepts and Findings, Oxford University Press, will be out in 2011.

sixhat network of research ramblings

Three ways

1. Spreadsheet editor
2. Importing
3. DL Language

Wang, H. & Wellma, B. (2010). Social Connectivity in America: Changes in Adult Friendship Network Size From 2002 to 2007. American Behavioral Scientist 53(8): 1148-1169. Abstract

There is some panic in the United States about a possible decline in social connectivity. The authors used two American national surveys to analyze how changes in the number of friends are related to changes in Internet use. The authors found that friendships continue to be abundant among adult Americans between the ages of 25 to 74 and that they grew from 2002 to 2007. This trend is similar among Internet nonusers, light users, moderate users, and heavy users and across communication contexts: offline, virtual only, and migratory from online to offline. Heavy users are particularly active, having the most friends both online and offline. Intracohort change consistently outweighs cohort replacement in explaining overall growth in friendship.

via American Behavioral Scientist