At InMobi daily we receive data about 800 million unique users and in-house Hadoop and spark platforms help us get meaningful insights from this data to drive a better targeted advertising model. This particular talk presents the architecture, data insights and the visualization of a ‘Location Based Social Graph’ we have built, pivoting around bssid, lat-long, essid of ad requests. Spark GraphX has enabled us to generate interesting network statistics at scale like degree distribution and connected components per geography and filter out the noisy edges. We are in the process of developing a home/work/public classification model to better understand nature of social cliques and the trending apps in each clique. In future we hope to overlay the graph with user attributes like demographics, app-affinity and segment-membership to derive meaningful relationships between users like Friends, Families or Colleagues and enable better CVR prediction based on look-alike modeling.