For many networks (e.g. opinion consensus, cooperative estimation, distributed learning and adaptation etc.) to proliferate and efficiently operate, the participating agents need to collaborate with each other by repeatedly sharing information which is often costly while brings no direct immediate benefit for the agents. In this paper, we develop a systematic framework for designing distributed rating protocols aimed at incentivizing the strategic agents to collaborate with each other by sharing information. The proposed incentive protocols exploit the ongoing nature of the agents' interactions to assign ratings and through them, determine future rewards and punishments through social reciprocation. Unlike existing rating protocols, the proposed protocol operates in a distributed manner, and takes into consideration the underlying interconnectivity of agents as well as their heterogeneity. We prove that in many deployment scenarios adopting the proposed rating protocols achieves full efficiency (i.e. price of anarchy is one) even with strategic agents.