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The ability to create groups has for long been one of the features of interest for the users across the social networks. Groups allow users (account holders) to join them, be a fan-follower of them, etc. In addition, there have been provisions for the account holder to also be a part of multiple groups as a member. The primary goal of our research is to analyze patterns within group membership. Essentially, we will be proposing a model based on market basket analysis over a subset of the social network groups and then derive a set of associative mining rules amongst these networks. The information mined may provide interesting insights for cross-marketing purposes. For instance, using this technique an organization Ox could find that the buyers of their product x tend to be socially fans or members of groups with the subtype Y or even, perhaps, more specifically with another organization Oy that sell a product y. Using such data, the organizations may then try to leverage these findings to build a co-opetative (Co-operation + Competition), profitable synergy and might also give them insights to determine which products of Oy may be impacted if the store discontinues selling the product x or vice-versa. The aim of this paper is to bring in a new high-level dimension to the social network analysis area by imbibing the concepts of Market Basket and then try to provide a basic feasibility criterion for the entire approach by simple simulations.