Name
Title | ||
|---|---|---|
Maximizing three-hop influence spread in social networks using discrete comprehensive learning artificial bee colony optimizer. |
Abstract | ||
|---|---|---|
Aiming at resolving the influence maximization (IM) problem in social networks, this paper proposes a three-layer-comprehensive-influence evaluation (TLCIE) model to measure the spread range of combinational nodes in the independent or weighted cascade models. The TLCIE is an enhanced three-hop influence spread model by integrating the intra- and inter-layer’s propagation effect, which improves the accuracy of propagation simulation and the reliability of parameter estimation. Then, an adaptive discrete artificial bee colony algorithm (ADABC) is devised to resolve the TLCIE model efficiently. In ADABC, the comprehensive-learning guided (CLG) updating rules, the degree-improvement initialization method and the semi-abandonment scout bee strategy are incorporated to enhance the search ability. Finally, the proposed model and algorithm are tested on a set of real-world social network instances, and the experimental results validate their effectiveness and efficiency. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.asoc.2019.105606 | Applied Soft Computing |
Keywords | Field | DocType |
Social networks,Influence maximization,Three-hop,Discrete artificial bee colony algorithm | Artificial bee colony algorithm,Mathematical optimization,Social network,Cascade,Initialization,Hop (networking),Estimation theory,Maximization,Mathematics | Journal |
Volume | ISSN | Citations |
83 | 1568-4946 | 1 |
PageRank | References | Authors |
0.37 | 0 | 2 |