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Abstract. Mining important persons is significant to network security and computer forensics nowadays in researches on email network centralization. Traditional PageRank algorithm is prone to be affected by interferential nodes because it distributes PR values evenly. This paper proposes a method which decomposes email network into different layers based on the core number, eliminates the interferential nodes in outer layers to decrease impact of interferential nodes and complexity of following procedure. Besides, it proposes an improved PageRank algorithm which partially solves the bias problem on nodes' weighting, ranks the nodes quantitatively. The experiments indicate that it improves the accuracy and reduces the computational complexity in mining important nodes from email network.

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Abstract. Based on Bayesian classification algorithm principle and implementation, propose an improved method of the algorithm. Firstly, instead of constant probability of spam, actual priori probability is used. Secondly, the selective range and rule of token is improved. Finally, add URLs and images into detection content. A mail recognizer based on improved Bayesian classification is designed. The experiment result shows that the improved Bayesian classification algorithm works well in practice.