Spectral partitioning: the more eigenvectors, the better
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Applying data mining in investigating money laundering crimes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Locating hidden groups in communication networks using hidden Markov models
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
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Financial crimes such as money laundering are often committed by cooperative individuals in a hidden manner. Discovering hidden group in financial transaction networks can help to find suspects of money laundering. A method is presented to discover the hidden group based on Hidden Markov Model (HMM) and genetic algorithm. HMM is used to describe financial transaction network. The maximum likelihood principle is adopted to transform hidden group detection to a combinational optimization problem. An effective genetic algorithm is devised to solve the optimization problem according to the characteristic of the feasible solutions. Real financial transaction data is preprocessed by considering multirelations among the accounts. Effectiveness and efficiency of our method is validated by experiments on both synthetic and real dataset.