Using DEA and Network Centrality to Measure Influence and Rank of Korean Stock Funds

Data envelopment analysis (DEA) is known as a useful tool which produces many efficient decision-making units (DMUs). Traditional DEA provides relative efficient scores and reference sets, but does not influence and rank for efficient DMUs. This paper suggests a method that provides influence and ranking information using PageRank centrality of Social Network analysis (SNA) based on reference sets and their lambda value. The social network structure expresses the DMU as a node, reference sets as link, and lambda as connection strength or weight. This paper shows PageRank centrality is more accurate than any others.