A Hurst-based diffusion model using time series characteristics for influence maximization in social networks

Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre-defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self-similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurst-based Diffusion Model (TSC-HDM), which calculates H based on the stationary or non-stationary characteristic of the time series. TSC-HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H > 0.5. The proposed model has been tested on four real-world OSN datasets. The results have been compared against four other IM models – Independent Cascade, Weighted Cascade, Trivalency, and Hurst-based Influence Maximization. TSC-HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC-HDM has attained 344% better average influence spread than other state-of-the-art models namely LIR, A-Greedy, LPIMA, Genetic Algorithm with Dynamic Probabilities, NeighborsRemove, DegreeDecrease, IGIM, IRR, and PHG.