Fighting criminals: Adaptive inferring and choosing the next investigative objects in the criminal network ?
When a criminal probabilistic network has been constructed, criminal investigators can access verified information about some nodes of network after investigations. Effective and efficient techniques are needed to help law enforcement and intelligence agencies to infer the state of other nodes and choose the next new investigative objects in the criminal network. In this paper, we propose a technique that employs a belief propagation algorithm to help criminal investigators to infer the criminal probability of other members by using verified partial information. In an updated criminal probabilistic network, this paper also presents a technique called EMPFS which extends the modified PFS algorithm. EMPFS algorithm is used to solve choice of next key investigative objects from criminal network. Experimental results show that the precision and efficiency of two techniques might be improved by exact constructing of the crime probabilistic network.
inference criminal network belief propagation partial information extended mpfs