Analyze Dynamics of a Social Network

The Wolfram Language can be used to analyze a wide range of complex data, including time-dependent metrics of social networks. With the help of a wide range of built-in time series processing functionality, you can analyze reputation scores and user counts of vegetarianism.stackexchange.com by importing and registering an EntityStore created from an archive of the site.

Look at the reputation score for a specific user.

Find the reputation score time series for users with the five highest reputation scores.

Visualize the reputation score time series for these users.

Find and aggregate the reputation histories for all users.

Find the user count time series, based on user creation dates:

Resample the two time series and compute the ratio of reputation score to total users.

Find the reputation histories for all users that have at least 25 data points.

Normalize the data in terms of days since account creation.

Fit the data to a power formula .

Perform statistical computations about the distribution of the power parameter .

Visualize the distribution of the power parameter in a histogram.

Look at the data for the outliers.

The largest outlier is a user who did not gain substantial reputation until quite awhile after creating their account.