Coalition, cryptography, and stability: mechanisms for coalition formation in task oriented domains
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Data mining: concepts and techniques
Data mining: concepts and techniques
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
Marginal contribution nets: a compact representation scheme for coalitional games
Proceedings of the 6th ACM conference on Electronic commerce
Group and topic discovery from relations and text
Proceedings of the 3rd international workshop on Link discovery
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A linear approximation method for the Shapley value
Artificial Intelligence
Determining the top-k nodes in social networks using the Shapley value
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Polynomial calculation of the Shapley value based on sampling
Computers and Operations Research
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient computation of the shapley value for centrality in networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
An approach for multi-objective categorization based on the game theory and Markov process
Applied Soft Computing
Community Discovery via Metagraph Factorization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-objective community detection in complex networks
Applied Soft Computing
Topic oriented community detection through social objects and link analysis in social networks
Knowledge-Based Systems
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The attribute information of individuals, such as occupation, skill, faith, hobbies and interests, etc, and the structure information amongst individuals, such as mutual relationships between individuals, are two key aspects of information that are used to study individuals and communities in social networks. Considering only the attribute information or the structure relationship alone is insufficient for determining meaningful communities. In this paper, we report an on-going study, we propose an approach that incorporates the structure information of a network and the attribute information of individuals by cooperative games, and game theory is introduced to support strategic decision making in deciding how to recognize communities in social networks, such networks are featured by large number of members, dynamic and with varied ways of connections. This approach provides a model to rationally and logically detect communities in social networks. The Shapley Value in cooperative games is adopted to measure the preference and the contribution of individuals to a specific topic and to the connection closeness of a coalition. We then proposed an iterative formula for computing the Shapley Value to improve the computation efficiency, related theoretical analysis has also been performed. Finally, we further developed an algorithm to detect meaningful communities.