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 preadopt 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, Erdős-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 the seed budget threshold above which the diffusion time is dramatically reduced, i.e., phase transition of diffusion time. Furthermore, based on our theoretical findings, we propose a practical seeding algorithm, called Practical Partitioning and Seeding PrPaS and demonstrate that PrPaS outperforms other baseline algorithms in terms of the diffusion speed over a real social network topology. We believe that our results provide new insights on how to seed over a social network depending on its connectivity structure, where individuals rationally adopt a new innovation.