Some Random Notes about the International Network Meeting 

 Last week we had an International Meeting on Methodology for Empirical Research on Social Interactions, Social Networks, and Health here at the IQ., thanks to the organization by Professor  Charles Manski  and Professor  Nicholas Christakis . Some people told me that the second day of the meeting was much more "dynamic and interactive" than the first day and based on what I have seen, I believe it was true. I saw at least three cliques of speakers were automatically formed on site along the disciplinary lines: statisticians, economists, and sociologists and political scientists. There were even sub-cliques and backfires! Fortunately, nobody was severely wounded. But anyway, it was a great intellectual exchange between disciplines. Below are some brief notes I took at the second day of the meeting, particularly at the last 20 minutes of the meeting when speakers talked about the future directions of network analysis in social sciences. Sorry for that I forgot to jot down exactly who said what, and that I also squeezed into the notes some of my personal thoughts. I took full responsibility for all errors in the notes.  


 1.	Need to combine game theory with social network analysis, particularly evolutionary game theory (and transaction costs theory).  

 2.	Need to further develop social network analysis based on (random) graph theory, typology and random matrix theory.  

 3.	Network studies tend to focus on network structure and typology as dependent variables while social sciences are more concerned with how network positions and features affect node level of problems. To put simply, network studies tend to start from nodes and end at network while social sciences are more like a top-down approach.  

 4.	In either case, however, it is very crucial to understand the data/tie generating mechanism. Especially, think that the formation of ties can go two ways: influence and selection. For example, smokers can become friends either because a person is influenced by his/her smoking friend to start smoking or because they are both smokers and then become friends. For another example, a highly educated person is usually less likely to be nominated by others as the best friend. This could be either because the highly educated person is less trustworthy or incapable to maintain friend ties or because he/she is more independent and less wiling to associate with others.Longitudinal data may help solve the influence vs. selection issue.  

 5.	Network analysis assumes that the probability of forming ties between nodes is the same between any pair of nodes. So start with a meaningful number of nodes to build network so that each node have roughly the same probability to form ties with one another.  

 6.	How the sever of an existing tie and the formation of a new tie will affect the structure of social network? How ties can bring more ties and lead to polarized network? Nonlinear generating processes and dynamics in network can lead to dramatic difference in network structure for any tiny changes at the node level. How network size can affect network structure? (Think about the difference among monopolistic market, oligarchic market and perfect competitive market.) 

 7.	How to define homophyly between friends? One dimension vs. multiple dimensions? Suppose it is one dimension, there are still two approaches: 1) do a mean test between the tie senders and the tie receivers. 2) Use the ratio of the number of ties whose connected nodes are in the same group (e.g., age +/- 5) that you defined to the total number of ties as an alternative measure. What else? 

 8.	Need to think about how to incorporate network analysis into traditional regression framework. We can either include network properties into regression models to study how network affect personal/clique level of phenomena or use regressions to evaluate how network properties are determined by socioeconomic variables.  

 9.	How to deal with the dependence structure among node level of variables since the errors are not iid.? Is it enough to just using correlation matrix to weight the standard errors and get robust SEs? 

 10.	Need to combine network software with traditional statistical software. The stat-net is getting there. But for Stata users, canned programs are needed to generate network data inside of Stata.  

 Lastly, for those of you who are interested in causal analysis, read Patrick Doreian (2001), "Causality in Social Network Analysis" (Sociological Methods and Research 30: 81-114) and see if you can improve upon his study.