Community Detection using a Measure of Global Influence

Abstract. The growing popularity of online social networks gave researchers access to large amount of network data and renewed interest in methods for automatic community detection. Existing algorithms, including the popular modularity-optimization methods, look for regions of the network that are better connected internally, e.g., have higher than expected number of edges within them. We believe, however, that edges do not give the true measure of network connectivity. Instead, we argue that influence, which we define as the number of paths, of any length, that exist between two nodes, gives a better measure of network connectivity. We use the influence metric to partition a network into groups or communities by looking for regions of the network where nodes have more influence over each other than over nodes outside the community. We evaluate our approach on several networks and show that it often outperforms the edge-based modularity algorithm. Key words: community, social networks,...

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Rumi Ghosh, Kristina Lerman
• Keywords: CORR 2008, Education, Network Connectivity, Online Social Networks, Social Networks