Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
SNAKDD 2008 social network mining and analysis postworkshop report
ACM SIGKDD Explorations Newsletter
Hierarchical feedback algorithm based on visual community discovery for interactive video retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Centrality metric for dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Where the blogs tip: connectors, mavens, salesmen and translators of the blogosphere
Proceedings of the First Workshop on Social Media Analytics
A non-dominated neighbor immune algorithm for community detection in networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Data-driven modeling and analysis of online social networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Towards hierarchical context: unfolding visual community potential for interactive video retrieval
Multimedia Tools and Applications
Understanding and leveraging tag-based relations in on-line social networks
Proceedings of the 23rd ACM conference on Hypertext and social media
Random walks based modularity: application to semi-supervised learning
Proceedings of the 23rd international conference on World wide web
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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.