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Machine Learning Based Method to Predict Influence Spread

Influence maximization problem is an extensively studied problem among researchers from diverse disciplines due to its broad range of applications i.e. maximizing information propagation, identification of most influential node, stopping disease spread etc. It deals with identifying most crucial nodes in the network, which once activated trigger a widespread of that behavior throughout the network. Influence maximization is an NP-Hard problem. Of various approximation algorithms available, heuristic based seed selection has attracted much attention due to its computational efficiency. Influence spread which is evaluated based on diffusion process is mostly measured using Monte Carlo simulations by many researchers. Diffusion is a stochastic process containing multiple iterations of estimations. However, Monte Carlo predictions are computationally complex for greater efficiency. Hence for complex network, this approach is inadequate for influence maximization. Even a modern computer will take days to solve influence maximization problem on complex networks. In this paper, we present an analysis of various heuristic based influence maximization techniques and also propose a machine learning based approach to find the spread of information in the network.