Social Capital Simulation Updated

The social capital simulation has been improved! The usability has been improved, preset examples have been added, and additional information in now reported.

Social Capital Simulation (Online)

The past couple days I have been working on an online social capital simulation that was created primarily with Javascript. Currently, it calculates social capital in the same manner as the excel version, however, it is more powerful as it allows you to set how many nodes you would like in the network, it dynamically creates a visual graph of the network, and it is accessible online.
I used Walter Zorn's High Performance JavaScript Vector Graphics Library to draw the network (i.e., nodes, lines, and text). This is an impressive library, which makes drawing with Javascript more pleasant than I originally expected. Also, to facilitate this project, I extended Zorn's library by adding getColor, getOpacity, and setOpacity methods. Furthermore, Michael Deardeuff and other Data Mining Lab members used their keen pattern finding skills to develop the mathematical equation for node placement in the graph.

Let me know how it works for you. I want to make this available so that it is easy to for people to get a feel for how we calculate social capital, which will allow us to refine our method.

Social Capital Simulation

Our recent work has explored the concept of social capital, which I have discussed previously. Our social capital metrics, namely bonding and bridging (popularized by Robert Putnam), utilize the hybrid network methodology that we have developed for online communities.

To understand our metrics, I have created a basic social capital simulation (an excel spreadsheet) having five nodes. The simulation allows for you to change the connection strengths in both the implicit affinity network (IAN) and the explicit social network (ESN). Changing these values will give you an idea of how social capital fluctuates as the social network changes.

The figure above shows the initial configuration of the simulation. The dashed blue lines represent the IAN and the solid pink lines represent the ESN. The thicker the lines the stronger the connection. The weights for the IAN were randomly assigned, while the ESN weights were all set to one, thus creating a clique.

Initially, the bonding and bridging social capital are both 1, since everyone in the network is connected. To see how the social capital fluctuates, change the blue and/or pink values, again representing the IAN and the ESN weights respectively, in the spreadsheet.

Topic Tool

Today, I put together a web page topic tool by using the web service that Nathan Davis made available. The tool takes in one or more web pages (i.e., a list of URLs) and then extracts the topics given the text on the web pages. The topics, or more accurately, the most likely topic components are extracted using an algorithm called Latent Dirichlet Allocation (LDA).

One potential use is for quickly generating blogger profiles to be used for implicit affinity networks. You can try it out at:

http://dml.cs.byu.edu/matthewsmith/tools/topictool/

Nathan uses his web service to make the query expansion service for Google searches called GooEgg.