This paper addresses the problem of pruning the gigantic social network retaining desirable properties, develops a criterion to form a final contributor graph with potential vertices and demonstrates the quality of a good trip design. Our model addresses issues concerning optimization of revenue for Online Travel Agency (OTA) while taking care of customers requirements as well. Our model discovers co-occurrence relationships among activities performed by (or recorded about) specific individuals or groups and correlate users with similar interests. The problem of finding minimum potential vertex set is computationally similar to standard NP-hard problems in graph theory like finding vertex cover, max-clique, etc. We propose a greedy approach to developing heuristic for final pruning and focus our model to provide a satisfiable good-enough solution - feasible to compute in polynomial time. We present a comprehensive summary of literature survey in domains of influence maximization, recommendation systems, etc in the context of marketing - with the emphasis on context-specific influence propagation. The results show the efficiency of our approach and substantiates our claim that this approach forms a conglomerate of social groups leading to a good trip design. Proposed model is flexible, generic and adaptable to address similar data-driven user-oriented problems.