StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

TL;DR

StaticGreedy is a novel influence maximization algorithm that guarantees submodularity, significantly reducing computation time while maintaining accuracy, and includes a dynamic update strategy for further speedup on large social networks.

Contribution

It introduces a static greedy algorithm that ensures submodularity, overcoming the scalability-accuracy dilemma in influence maximization, with substantial efficiency improvements.

Findings

Reduces computational expense by two orders of magnitude.

Guarantees submodularity during seed selection.

Achieves 2-7 times speedup with dynamic updates.

Abstract

Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy. In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.