Trust Network and Trust Community Clustering based on Shortest Path Analysis for E-commerce

Trust in e-commerce has become one of the most important issues in online applications. Constantly, a user will only search for the most credible of goods and service providers and then take on their transactions. How to confirm which service providers are the most trusted for a user has become the most critical problems. This paper presents a trust network and trust community clustering for the analysis of the users most trusted relationship. It uses the nodes to represent the various subjects involved in the trust and use the connection links to denote relationships. The weight of the links indicates the strength of the relationships. First, it construct a trust network diagram which has the weight value of links, and then to analyze the clustering properties of the relationship according to the weights and the path length. At last, it classifies the most trusted subjects to the same cluster for a user. Direct trust information degree and global trust information degree are used to evaluate trust relations among subjects and it gives an improved shortest path algorithm to construct trust network. A clustering algorithm based on coefficient and path length is presented for E-commerce trust network community. Experiments show that the method of building trust through the network model can well describe the main indirect E-commerce trust and the algorithm has obvious advantages in accuracy and time cost.

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