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The steady growth of graph data from social networks has resulted inwide-spread research in finding solutions to the influence maximizationproblem. In this paper, we propose a holistic solution to the influencemaximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI)model that closely mirrors the real-world scenarios. Under the OI model, weintroduce a novel problem of Maximizing the Effective Opinion (MEO) ofinfluenced users. We prove that the MEO problem is NP-hard and cannot beapproximated within a constant ratio unless P=NP. (2) We propose a heuristicalgorithm OSIM to efficiently solve the MEO problem. To better explain the OSIMheuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, ascalable algorithm capable of running within practical compute times oncommodity hardware. In addition to serving as a fundamental building block forOSIM, EaSyIM is capable of addressing the scalability aspect - memoryconsumption and running time, of the IM problem as well.  Empirically, our algorithms are capable of maintaining the deviation in thespread always within 5% of the best known methods in the literature. Inaddition, our experiments show that both OSIM and EaSyIM are effective,efficient, scalable and significantly enhance the ability to analyze realdatasets.

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models