With the proliferation of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment sharing content (bookmark, photos, videos), and users can tag her own favorite content. Users can also connect to friends, and subscribe to or become a fan of other users. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members focusing on similar topics. It is challenging to effectively integrate the network information of multiple dimensions to find out the cross-dimension group structure. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.