There are currently, thousands of online social networks (OSN) available, each of which hosts millions of users. Users make new friendship links, join groups on diverse topics, share opinions and thus help in building a big knowledge repository. In this paper we study the problem of defining proximity measure between groups (communities) of OSN. Understanding the proximity among the groups in OSN can reveal new insights into the structure of the network. We use this proximity measure in a novel way with other structural features of online groups to predict the evolving state of the network. One of the important task in the sphere of OSN is helping users in selecting groups of their interest, by making recommendations. We utilize group proximity measure to propose a new framework for recommending groups to users of OSN. Finally we investigate the effectiveness of our methods in three real OSN: Flickr, Live Journal and You Tube. Our methods give promising results.

@InProceedings{saha:snakdd08,
  author       = "Saha, Barna and Getoor, Lise",
  title        = "Group Proximity Measure for Recommending Groups in Online Social Networks",
  booktitle    = "2nd ACM SIGKDD Workshop on Social Network Mining and Analysis (SNA-KDD)",
  year         = "2008",
}