A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based Ones. In this paper, we consider a game-based model, where each Individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We address the following two questions on the diffusion speed of a new Innovation under the game-based model: 1) what is a good subset Of individuals to seed for reducing the diffusion time significantly, i.e., convincing them to readopt a new innovation and 2) how Much diffusion time can be reduced by such a good seeding. For 1), we design near-optimal polynomial-time seeding algorithms for three representative classes of social network models, Red ˝os-Rényi, planted partition and geometrically structured Graphs, and provide their performance guarantees in terms of Approximation and complexity. For 2), we asymptotically quantify the diffusion time for these graph topologies; further derive Insights on how to seed over a social network depending on its Connectivity structure, where individuals rationally adopt a new Innovation.