Abstract
Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS (dynamic-reverse reachable set) influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses an automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range but also greatly reduces the time complexity. The experimental results on the two real datasets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF (cost-effective lazy-forward) algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm, and pBmH (population-based metaheuristics) algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large-scale social network.
Highlights
Because the rapid development of social networks, the number of users, and the scale of information dissemination continue to expand, the problem of maximizing influence has received more and more attention
We propose a dynamic-reverse reachable set (D-Reverse Influence Sampling (RIS)) algorithm based on reverse reachable set. e algorithm does not need to preset the theoretical threshold of the number of reverse reachable sets in advance but based on the monotonicity and submodularity of the influence propagation function, set the judgment conditions for generating the critical value of the random reverse reachable set, and automatically debugs the generation A certain number of reverse reachable sets can avoid time wastage while obtaining a better influence spread range
E RIS algorithm avoids the limitation of the high time complexity of the greedy algorithm and solves the problem that the heuristic algorithm lacks theoretical guarantee and cannot obtain the optimal solution. This algorithm cannot effectively control the number of random RR sets. ey proposed a threshold-based method to generate random RR sets: when the total number of generated nodes and edges reaches a predetermined theoretical threshold, they stop generating random RR sets. This method has approximately linear time complexity, there is a great correlation between the generation of reverse reachable sets of fixed theoretical thresholds, and the hidden constants in practice are large, resulting in two shortcomings in the RIS algorithm. (1) e actual RR set sample size generated is greater than the theoretical threshold. (2) ere is no guarantee that the theoretical threshold is the minimum number of samples generated in the RR set. erefore, the sample size of the RR set selected by this algorithm is not accurate, and it is not well suited for solving large-scale social networks
Summary
Because the rapid development of social networks, the number of users, and the scale of information dissemination continue to expand, the problem of maximizing influence has received more and more attention. E RIS algorithm avoids the limitation of the high time complexity of the greedy algorithm and solves the problem that the heuristic algorithm lacks theoretical guarantee and cannot obtain the optimal solution This algorithm cannot effectively control the number of random RR sets. Ey proposed a threshold-based method to generate random RR sets: when the total number of generated nodes and edges reaches a predetermined theoretical threshold, they stop generating random RR sets This method has approximately linear time complexity, there is a great correlation between the generation of reverse reachable sets of fixed theoretical thresholds, and the hidden constants in practice are large, resulting in two shortcomings in the RIS algorithm. This method has approximately linear time complexity, there is a great correlation between the generation of reverse reachable sets of fixed theoretical thresholds, and the hidden constants in practice are large, resulting in two shortcomings in the RIS algorithm. (1) e actual RR set sample size generated is greater than the theoretical threshold. (2) ere is no guarantee that the theoretical threshold is the minimum number of samples generated in the RR set. erefore, the sample size of the RR set selected by this algorithm is not accurate, and it is not well suited for solving large-scale social networks
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