• **Mathoverflow** has been studied as a "complex network" in <A HREF="https://www.researchgate.net/publication/283052716_Social_Achievement_and_Centrality_in_MathOverflow">Social achievement and centrality in MathOverflow</A>, by L.V. Montoya, A. Ma, and R.J. Mondragón.    
The analysis distinguishes *degree centrality* (based on the number of edges that a node has), *betweenness centrality* (which measures the fraction of geodesic paths that pass through a node), *closeness centrality* (the mean geodesic distance from a node to every other node), and *eigenvector centrality* (which measures how well connected a node is and how much direct influence it may have over other well connected nodes in the network). Three hypotheses that are tested (the first two pass, the third fails):

 1. A user’s reputation score is closely related to their degree centrality.
 2. The total number of views obtained by a user is related to their eigenvector centrality.
 3. The number of upvotes obtained by a user is related to their closeness centrality.

• **MathSciNet** has been used by Jerrold W. Grossman to analyze the network of collaborations among mathematics in <A HREF="https://www.siam.org/pdf/news/485.pdf">Patterns of Collaboration in Mathematical Research:</A> *Apparently, the appropriate popular buzz phrase for mathematicians should be “eight degrees of separation”*.    
See also <A HREF="http://math.buffalo.edu/mad/stats/2005.research.patterns.pdf">Patterns of Research in Mathematics </A> by the same author.