# The Evolution of Wikipedia's Norm Network
## 3 Results
### Emergent Clusters  
The connected component of network, containing 95% of all nodes, partitions into 10 clusters. In Table 2, we describe the top nine, which together nearly all of the giant component. By inspecting the top ten nodes in each cluster, we classify them into user-content, user-user, and user-admin norms (see Table F.2). A force-directed layout (ForceAtlas2, implemented in Gephi [58]) allows us to visualize the norm network and the topological relationships between its emergent groups (see Figure 4).  
Figure 3: Evolution of influence overlap among the core norms (top twenty norms by eigenvector centrality) over time (solid line, labeled). In terms of the pages they influence, core norms draw apart over time, stabilizing in 2008. At the same time, semantic coherence (dashed line, labeled) increases. Neighborhoods become topologically distinct, but internally coherent.  
The five largest clusters comprise roughly 90% of the network. The Article Quality cluster includes nodes such as Neutral Point of View, Verifiability, and Reliable Sources, governing how articles should be written. The Collaboration cluster includes pages on Consensus, Assume Good Faith, and Edit Warring, describing policies and norms associated with interpersonal interaction. The Administrators cluster contains pages relevant to administrative actions, such as the Blocking Policy and the Arbitration Committee. The Formatting cluster contains articles such as Manual of Style, Article Titles, and Disambiguation. Additionally, the Content Policies cluster contains articles on copyrights, copyright violations, and policies on image use and use of non-free content. The remaining  
\begin{table}
\begin{tabular}{c c l l} \hline \hline
**Rank** & **Fraction of System** & **Classification** & **Topic** \\ \hline
1 & 24.8\% & User-Content & Article Quality \\
2 & 22.9\% & User-User & Collaboration \\
3 & 17.1\% & User-Administration & Administrators \\
4 & 14.7\% & User-Content & Formatting Articles \\
5 & 10.5\% & User-Content & Content Policies \\
6 & 5.4\% & User-User & Wiki-larping \\
7 & 2.0\% & User-Content & Page Templates \\
8 & 1.3\% & User-User/User-Content & Experts and Credentials \\
9 & 1.0\% & User-User & Humor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top nine Louvain clusters, by number of nodes. Communities fall into three classifications (user-user, user-content, user-administration), based on the interactions they govern; we determine these labels by inspecting the top ten nodes by centrality within each cluster.  
Figure 4: The topology of the norm network is organized around five central clusters, found using the Louvain algorithm. Cluster themes are based on a sample of high-eigenvector centrality (EC) nodes in each cluster and confirmed by reference to a topic model of word usage. Left panel: full network, with cluster membership indicated by color. Right panel: cluster structure. Each node is a Louvain cluster, and node size indicates cluster size by number of pages. Edge weights are defined as the fraction of the origin cluster’s out-links that link to each other cluster (self-loops are not shown).  
clusters include a small group of articles on page templates; one on the role of experts of Wikipedia; two groups of humor pages (Wiki-larping, a humorous take on Wikipedia as if it were a Dungeons and Dragons game, and a cluster of pages, including \"Bad Jokes and Other Deleted Nonsense\").  
Each of the top nine clusters is associated with a distinct topic in our topic model (see Appendix F, Table F.1); while the article quality cluster is the largest by node number, the topic associated with the collaboration cluster dominates the system by word. Even task-based norms appear to draw on the semantics of interpersonal cooperation.