Because of the advantages of graphs in visualizing the relationship between individuals, complex networks have been widely used and greatly developed. In real-world applications of Dempster–Shafer evidence theory, there are usually thousands of sensors collecting information. It is easy to be overwhelmed by the mass of information and ignore the connections between them. The rise of the semisupervised learning method graph convolutional network makes it possible to address this issue. In this article, inspired by complex network, the basic probability assignment function, the base function of evidence theory, is modeled in a novel form of the network graph. Some typical issues of evidence theory, such as conflicting evidence, multiclass evidence clustering, and computational complexity for large-scale fusion are systematically addressed in the framework of the proposed network model. What's more, a new combination rule is presented from the point view of the graph. The empirical results of experiments on real data set demonstrate the potential and feasibility of complex networks in traditional evidence theory.
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