Rapid growth in social networks (SNs) presents a unique scalability challenge for SN operators because of the massive amounts of data distribution among large number of concurrent online users. A request from any user may trigger hundreds of server activities to generate a customized page and which has already become a huge burden. Based on the theoretical model and analytical study considering realistic network scenarios, this article proposes a hybrid P2P-based architecture called PAIDD. PAIDD fulfills effective data distribution primarily through P2P connectivity and social graph among users but with the help of central servers. To increase system efficiency, PAIDD performs optimized content prefetching based on social interactions among users. PAIDD chooses interaction as the criteria because user’s interaction graph is measured to be much smaller than the social graph. Our experiments confirm that PAIDD ensures satisfactory user experience without incurring extensive overhead on clients’ network. More importantly, PAIDD can effectively achieve one order of magnitude of load reduction at central servers.