The popularization of GPS-enabled mobile devices provides social network researchers a taste of cyber-physical social network in advance. Traditional link prediction methods are designed to find friends solely relying on social network information. With location and trajectory data available, we can generate more accurate and geographically related results, and help web-based social service users find more friends in the real world. Aiming to recommend geographically related friends in social network, a three-step statistical recommendation approach is proposed for GPS-enabled cyber-physical social network. By combining GPS information and social network structures, we build a pattern-based heterogeneous information network. Links inside this network reflect both people's geographical information, and their social relationships. Our approach estimates link relevance and finds promising geo-friends by employing a random walk process on the heterogeneous information network. Empirical studies from both synthetic datasets and reallife dataset demonstrate the power of merging GPS data and social graph structure, and suggest our method outperforms other methods for friends recommendation in GPS-based cyberphysical social network.