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We apply our technology to network pharmacology by constructing herb-compound-putative target network; compound-putative targets-RA target network; and imbalance multi-level herb-compound-putative target-RA target-PPI network. We then identify the major targets in the network based on our centrality measure approach. Finally we validated the major identified network targets using the enrichment analysis and a molecular docking simulation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Result<\/jats:title><jats:p>The results reveled our proposed weighted network centrality approach outperform classical centrality measure in identification of influential nodes in four real complex networks based on SI model simulation. Application of our approach to network pharmacology shows that 57 major targets of which 33 targets including 8 compositive compounds, 15 putative target and 10 therapeutic targets played an important role in the network and directly linked to rheumatoid arthritis. Enrichment analysis confirmed that putative targets were frequently involved in TNF, CCR5, IL-17 and G-protein coupled receptors signaling pathways which are critical in the progression of rheumatoid arthritis. The molecular docking simulation indicated four targets had significant binding affinity to major protein targets. Glyceryl diacetate-2-Oleate and Oleoyl chloride showed the best binding affinity to all targets proteins and were within Lipinski limits. ADMET prediction also confirm both compounds had no toxic effect on human hence potential lead drug compounds for treating rheumatoid arthritis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study developed an efficient weighted network centrality approach as tool for identification of major network targets. Network pharmacology findings provides promising results that could lead us to design and discover of alternative drug compounds. 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