AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Mining communities in networks: a solution for consistency and its evaluation
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Feature relationships hypergraph for multimodal recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
A better strategy of discovering link-pattern based communities by classical clustering methods
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A framework for exploring organizational structure in dynamic social networks
Decision Support Systems
Detecting communities in K-partite K-uniform (hyper)networks
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
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Learning communities from a graph is an important problem in many domains. Different types of communities can be generalized as link-pattern based communities. In this paper, we propose a general model based on graph approximation to learn link-pattern based community structures from a graph. The model generalizes the traditional graph partitioning approaches and is applicable to learning various community structures. Under this model, we derive a family of algorithms which are flexible to learn various community structures and easy to incorporate the prior knowledge of the community structures. Experimental evaluation and theoretical analysis show the effectiveness and great potential of the proposed model and algorithms.