A probability-driven structure-aware algorithm for influence maximization under independent cascade model

Influence maximization (IM) is the problem of finding a set of nodes that can achieve the maximal influence spreads into the network, which faces two significant but intractable issues in latest studies: (i) Curse of scales: with the increase of the network scale, traditional methods cost extensive times in guaranteeing accuracy, which re-evaluate influence spread of every node in network, leading to significant computational overhead; (ii) Generalization issue: with more and more studies on various networks and propagation parameters, it is difficult to find a universally appropriate algorithm that performs well in each topology. In this paper, we propose a novel probability-driven structure-aware (PDSA) algorithm, which begins by cutting/updating network according to the edge activation probability parameters of the IC model, and then uses a graph traversal algorithm (e.g., breadth first search algorithm) to evaluate the influence spread scores of each node. Meanwhile, we adopt a kind of centrality-based independent cascade (CIC) model to approximate a more realistic propagation scenario. Through extensive experiments with six real-world/synthetic networks and six CIC/IC models, we demonstrate that PDSA achieves great performance over state-of-the-art algorithms in terms of effect and efficiency. Even facing various complex topologies and propagation parameters, PDSA exhibits excellent robustness in solving IM problems. •Efficient algorithm to solve the IM problem on large-scale networks.•The novel structure-aware algorithm adaptively finds the influential nodes.•A centrality-based IC model is applied to simulate more realistic social scenarios.•The proposed algorithm achieves great performance on both effect and efficiency.•Our methodology universes for various topologies and propagation parameters.