Robustness of Influence Maximization Against Non-adversarial Perturbations

Influence maximization problem has been extensively studied. Given a social network, an influence maximization algorithm aims to find a set of influential (seed) nodes in the network such that the expected number of nodes influenced by the seed nodes is maximized under the given cascade model. Most influence maximization algorithms proposed in the literature assume that ground-truth influence spread probabilities between nodes are available. In reality, however, it is natural to assume that there exists a deviation of the influence spread probability used in the influence maximization algorithms from actual influence spread probability. In this paper, we examine the robustness of existing influence maximization algorithms against non-adversarial perturbations in influence spread probabilities. Existing work has investigated the worst-case effectiveness of influence maximization algorithms subject to adversarial perturbations. In contrast, we consider three types of non-adversarial perturbations and investigate the effectiveness of influence maximization algorithms for finding influential nodes in a more relaxed scenario. Our results show that, even in the context of non-adversarial perturbations, the effectiveness of the state-of-the-art approximation and heuristic algorithms may be significantly degraded and lightweight heuristic algorithms can outperform state-of-the-art algorithms when the perturbations are large.

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