The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.
Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.
With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.
This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.
Wang, W., Wang, J., Huang, X. and Shen, Q. (2011), "Study on community structure characteristics of cluster networks with calculation and adjustment of trust degree based on the grey correlation degree algorithm", Grey Systems: Theory and Application, Vol. 1 No. 2, pp. 129-137. https://doi.org/10.1108/20439371111163756
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