Rice (Oryza sativa L.) is one of the essential staple food crops and tillering, panicle branching and grainfilling are three important traits determining the grain yield. Although miRNAs have been reportedbeing regulating yield, no study has systematically investigated how miRNAs differentially function inhigh and low yield rice, in particular at a network level. This abundance of data from high-throughputsequencing provides an effective solution for systematic identification of regulatory miRNAs usingdeveloped algorithms in plants. We here present a novel algorithm, Gene Co-expression Networkdifferential edge-like transformation (GRN-DET), which can identify key regulatory miRNAs in plantdevelopment. Based on the small RNA and RNA-seq data, miRNA-gene-TF co-regulation networkswere constructed for yield of rice. Using GRN-DET, the key regulatory miRNAs for rice yield werecharacterized by the differential expression variances of miRNAs and co-variances of miRNA-mRNA,including osa-miR171 and osa-miR1432. Phytohormone cross-talks (auxin and brassinosteroid) werealso revealed by these co-expression networks for the yield of rice.