The popularity of social media has led many users to create accounts with different online social networks. Identifying these multiple accounts belonging to same user is of critical importance to user profiling, community detection, user behavior understanding and product recommendation. Nevertheless, linking users across heterogeneous social networks is challenging due to large network sizes, heterogeneous user attributes and behaviors in different networks, and noises in user generated data. In this paper, we propose an unsupervised method, Collective Network Linkage (CNL), to link users across heterogeneous social networks. CNL incorporates heterogeneous attributes and social features unique to social network users, handles missing data, and performs in a collective manner. CNL is highly accurate and efficient even without training data. We evaluate CNL on linking users across different social networks. Our experiment results on a Twitter network and another Foursquare network demonstrate that CNL performs very well and its accuracy is superior than the supervised Mobius approach.