Abstract: We investigate the structure of genomic interaction to understand cellular process using vast amount of microarray data. Instead of observing individual expression of genes, which may not possess the explicit understanding of the purpose or intension of what they perform in the context of cellular process, we apply the wisdom of social network analogy to the gene expression profile. Dichotomized bipartite matrices and bipartite graphs were created to identify the social star genes and the network structures from the Rosetta Compendium dataset with 300 diverse mutations and chemical treatments in S. crevisiae using various measures such the centrality indices, introduced and developed in social network analysis. We found star genes and core conditions using core-periphery structure and betweenness centrality index, which reflect the global information of the network. We also divided yeast genomes by MIPS functional classification and calculated group centralities for each category of MIPS classification to investigate the subnetworks relevant to the core-periphery structure.