Version 1
: Received: 7 February 2021 / Approved: 8 February 2021 / Online: 8 February 2021 (14:18:57 CET)
How to cite:
Sun, G.; Chen, C. Influence Maximization Algorithm Based on Reverse Reachable Set. Preprints2021, 2021020213. https://doi.org/10.20944/preprints202102.0213.v1
Sun, G.; Chen, C. Influence Maximization Algorithm Based on Reverse Reachable Set. Preprints 2021, 2021020213. https://doi.org/10.20944/preprints202102.0213.v1
Sun, G.; Chen, C. Influence Maximization Algorithm Based on Reverse Reachable Set. Preprints2021, 2021020213. https://doi.org/10.20944/preprints202102.0213.v1
APA Style
Sun, G., & Chen, C. (2021). Influence Maximization Algorithm Based on Reverse Reachable Set. Preprints. https://doi.org/10.20944/preprints202102.0213.v1
Chicago/Turabian Style
Sun, G. and Chih-Cheng Chen. 2021 "Influence Maximization Algorithm Based on Reverse Reachable Set" Preprints. https://doi.org/10.20944/preprints202102.0213.v1
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 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 automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range, and greatly reduce the time complexity. The experimental results on the two real data sets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm and pBmH 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.
Keywords
social networks; influence maximization; information diffusion model; reverse reachable set; sub-modularity
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
