Name
Title | ||
|---|---|---|
Learning Automata Based Approach for Influence Maximization Problem on Social Networks |
Abstract | ||
|---|---|---|
Influence maximization problem aims at targeting a subset of entities in a network such that the influence cascade being maximized. It is proved to be a NP-hard problem, and many approximate solutions have been proposed. The state-ofart approach is known as CELF, who evaluates the marginal influence spread of each entity by Monte-Carlo simulation and picks the most influential entity in each round. However, as the cost of Monte-Carlo simulations is in proportion to the scale of network, which limits the application of CELF in real-world networks. Learning automata (LA) is a promising technique potential solution to many engineering problem. In this paper, we extend the confidence interval estimator based learning automata to S-model environment, based on this, an end-to-end approach for influence maximization is proposed, simulation on three real-world networks demonstrate that the proposed approach attains as large influence spread as CELF, and with a higher computational efficiency. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/DSC.2017.54 | 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC) |
Keywords | Field | DocType |
influence maximization problem,social networks,NP-hard problem,Monte-Carlo simulation,CELF,confidence interval estimator based learning automata,S-model environment,end-to-end approach,LA | Convergence (routing),Monte Carlo method,Social network,Learning automata,Computer science,Greedy algorithm,Cascade,Artificial intelligence,Maximization,Estimator | Conference |
ISBN | Citations | PageRank |
978-1-5386-1601-7 | 0 | 0.34 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Ge | 1 | 18 | 3.65 |
| Jinchao Huang | 2 | 0 | 2.37 |
| Chong Di | 3 | 0 | 1.69 |
| Jian-hua Li | 4 | 558 | 98.16 |
| Shenghong Li | 5 | 357 | 47.31 |