Information-centric networking extensively uses universal in-network caching. However, developing an efficient and fair collaborative caching algorithm for selfish caches is still an open question. In addition, the communication overhead induced by collaboration is especially poorly understood in a general network setting such as realistic ISP and Autonomous System networks. In this paper, we address these two problems by modeling the in-network caching problem as a Nash bargaining game. We show that the game is a convex optimization problem and further derive the corresponding distributed algorithm. We analytically investigate the collaboration overhead on general graph topologies, and theoretically show that collaboration has to be constrained within a small neighborhood due to its cost growing exponentially. Our proposed algorithm achieves at least 16% performance gain over its competitors on different network topologies in the evaluation, and guarantees provable convergence, Pareto efficiency and proportional fairness.