Use of Eigenvector Centrality to Rank the Vertices in a Disease-Disease Network

We investigate the use of the eigenvector centrality (EVC) metric to rank the vertices in a disease-disease network built from the results of the disease-gene association studies reported in the NIH GWAS catalog and OMIM database. The vertices in the disease-disease network are the diseases and there exists an edge between two vertices if the corresponding diseases share at least one gene in the disease-gene association network. The weight of an edge in the disease-disease network is the number of shared genes between the end vertices (diseases) of the edge. The EVC value (ranging from 0 to 1) of a vertex/disease in such an undirected weighted graph comprehensively captures the impact of the number of edges incident on the vertex and the weights of these edges as well as the number of edges incident on the neighbors of the vertex and the weights of these edges. The distribution of the EVC values of the vertices exhibit a Pareto pattern (80-20 rule) such that only about 18% of the diseases have higher and appreciably different EVC values and the remaining 82% of the diseases have lower and comparable EVC values.