How Network Graphs are Generated? 

 When Professor  Nicholas Christakis  came by to give a talk on social networks and health two weeks ago,  some commentator  expressed concern about the sparseness of information contained in network graphs  (not specifically regarding Nicholas’ research, which I believe was well-done).  I do share the same concern with that commentator.  So afterwards I did some preliminary search on the literature about visualization of network data and found several interesting pieces that may help clarify (or even exacerbate) part of the concern some of us are having with network graphs. 


 The first is the lecture notes Professor  Peter V. Marsden  wrote about visualization of network graphs in soc275. Here I just want to highlight a few points in his notes. (Words in quotes are taken from Professor Marsden’s lecture notes.)  

 1) Network graphs can be “referenced to known geographical/spatial/social locations of points”.   

 2) Aesthetic criteria are used to generate network graphs, for examples, to minimize crossing lines, to make lines shorter, …  and “[to] construct plot such that close vertices are connected, positively connected, strongly connected, or connected via short geodesics”.  

 3) “Location of points reflects ‘social distances’”. …  “Spatial configuration differs depending on what 'distance-generating mechanism' is assumed and built in to one’s data.”  

 4) Some often-used network graph generating algorithms include factor analysis, multidimensional scaling (MDS) and spring embedders, etc.  

 So the configuration of network graphs seems to a large degree dependent on researchers’ theoretical interests and can change according to the network measures (whether it is the number of clusters within network or overall network connectedness, etc.) that researchers are mostly interested in.  In other words, before generating any network graphs, researchers have to be clear about what theoretical themes they aim to present through network graphs and then select corresponding network measures and generating algorithms. For those of you who want to follow up with this topic, there are several pieces recommended by Professor Marsden in his lecture notes that I think are good starting references. See below for more details.  

  
1. Bartholomew, David J., Fiona Steele, Irini Moustaki, and Jane I. Galbraith. 2002. The   Analysis and Interpretation of Multivariate Data for Social Scientists  . London: Chapman and Hall/CRC. Chapters 3 and 4. 

 2. Freeman, Linton C. 2005. “Graphic Techniques for Exploring Social Network Data.” Chapter 12 in Carrington, Peter J., John Scott, and Stanley Wasserman. 2005.     Models and Methods in Social Network Analysis    . New York: Cambridge University Press. 

 3. Freeman, Linton C. 2000. “Visualizing Social Networks.”   Journal of Social Structure 1  . (Electronically available at  http://www.cmu.edu/joss/content/articles/volindex.html )