Complex Network Method Towards Evaluating Industrial Symbiosis

Abstract

The construction of industrial park has become one of the main ways of cleaner production, energy saving and emission reduction in industrial development. Most industrial symbiosis (IS) networks are very complex. A method of evaluating IS networks is proposed based on complex network theory. Firstly, according to TOPSIS (technique for order preference by similarity to an ideal solution), taking the multi-attribute decision-making method to identify key enterprises in IS networks. Secondly, it realizes quantitative detection by the community partition of complex network. Finally, we evaluate the results of detection based on Girvan-Newman modularity and the cohesion of communities. The case analysis shows that the multi-attribute decision-making method can identify core enterprises effectively. The quantitative detection for community structure of IS networks is feasible with the method of complex network community detection. The integration degree of IS networks can be evaluated from the aspects of nodes and community structures.