Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
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Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
Towards comprehensive support for organizational mining
Decision Support Systems
Semantic Technology for Capturing Communication Inside an Organization
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Topic and role discovery in social networks with experiments on enron and academic email
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New spectral methods for ratio cut partitioning and clustering
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Spectral embedding for dynamic social networks
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Decision Support Systems
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Recent research has provided promising results relating to discovering communities within a social network. We find that further representing the organizational structure of a social network is an interesting issue that helps gain better understandings of the social network. In this paper, we define a data structure, named Community Tree, to depict the organizational structure and provide a framework for exploring the organizational structure in a social network. In this framework, an algorithm, which combines a modified PageRank and Random Walk on graph, is developed to derive the community tree from the social network. In the real world, a social network is constantly evolving. In order to explore the organizational structure in a dynamic social network, we develop a tree learning algorithm, which employs tree edit distance as the scoring function, to derive an evolving community tree that enables a smooth transition between two community trees. We also propose an approach to threading communities in community trees to obtain an evolution graph of the organizational structure, by which we can reach new insights from the dynamic social network. The experiments conducted on synthetic and real dataset demonstrate the feasibility and applicability of the framework. Based on the theoretical outcomes, we further apply the proposed framework to explore the evolution of organizational structure with the 2001 Enron dataset, and obtain several interesting findings that match the context of Enron.