| Summary: | Data envelopment analysis (DEA) is known as a useful tool that produces many efficient decision-making units (DMUs). Traditional DEA provides relative efficient scores and reference sets, but does not influence and rank the efficient DMUs. This paper suggests a method that provides influence and ranking information by using PageRank as a centrality of Social Network analysis (SNA) based on reference sets and their lambda values. The social network structure expresses the DMU as a node, reference sets as link, and lambda as connection strengths or weights. This paper, with PageRank, compares the Eigenvector centrality suggested by Liu, et al. in 2009, and shows that PageRank centrality is more accurate.
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