We model information diffusion through social networks using a game-theoretic paradigm. Our work focuses on the pairwise interactions between individuals and their social contacts, allowing each agent to make local decisions to maximize individual gain. This fully distributed approach is driven only by local utility and differs from many existing models that treat diffusion as a network process that occurs passively. Agents are inherently selfish, acting only to benefit from obtaining new information and from providing contacts with information that is new to them. Framed using game theory on graphs, we present a model that allows for parameterization of individual preference and models of pairwise interaction. We observe the effects of graph structure, incomplete information, and sharing cost on the model. We show that spatially organized graphs, due to their degree distribution, are much more resilient to higher costs of sharing. Additionally, we show how incomplete information often leads to more active agents at the cost of individual payoff. Finally, we provide insight into a number of extensions to this model that will allow for simulation of various diffusion phenomenon.