Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Overlapping Groups in Social Media
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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This article proposes a new model for clustering individual nodes based on node's interrelation with a real-life mining application. The model is capable of detecting a network topology based on information flow and therefore could be easily extended and applied in a variety of today's research fields. E.g. discover audience group sharing similar attitude, or retrieve authors' academic referencing group or plot active friend society in social networks. An effective algorithm: Boundary Growth Algorithm is proposed through which people can find the underlying structure of networks. Extensive experimental evaluations demonstrate the effectiveness of our approach.