This presentation covers the application of social network analysis when growing online communities.

4. Step 2: Score the Engagement Highly Engaged Members Recency + Frequency + Number of Friends = RFF Score Moderately Engaged Members Less Engaged Members 7 3 1 3 605 40092 75 1872847 7 2 3 2 724 40090 81 1362223 13 7 2 4 449 40091 66 1489879 14 1 4 9 750 40089 40 2000503 16 6 9 1 463 40084 218 1234567 17 5 7 5 512 40086 54 1617535 17 4 6 7 583 40087 46 1745191 19 8 5 6 195 40088 47 2128159 25 9 8 8 174 40085 45 2255815 RFF SCORE Rank of Visits Rank of Last Visit Rank of FRIEND ID Visits Last Visit NUM OF FRIENDS MEMBER ID

6. Step 4: Influencer Analysis A different set of metrics, a different conversation Degree centrality Degree centrality is defined as the number of direct connections a node has. Clustering coefficient The clustering coefficient quantifies how close the vertex and its neighbors are to being a clique (complete graph). Betweeness centrality A node with high betweenness has great influence over the information that flows through the network, conversely, these are single points of failure if these users disengage from the network. Closeness centrality The closeness centrality of a vertex is the average distance, along the shortest path, between the vertex and all other vertices reachable from it. Social Network Analysis

7. Identify Influencer Networks Used open source software NodeXL to measure and visualize the Military Advantage community and identify communication types and patterns. Answer Person: Outward Ties and relative absence of triangles. The Broker: A user that connects influential networks to each other Cliques: The closer the clustering coefficient is to 1 the more complete graph and the tighter the clique.