Name
Abstract | ||
|---|---|---|
Due to its popularity, influence propagation model has been recently exploited in several social network applications. However, there are some limitations in applying the model to the social network, in which negative information is propagated. In this paper, we present an effective information propagation model to overcome these limitations. Our minimum cost flow model effectively propagates influences to neighbouring nodes with minimum costs in each path of the social network. The model removes noise associated with social network marketing information and propagates influences without overlapping in information nodes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CGC.2012.64 | CGC |
Keywords | Field | DocType |
minimum cost,propagation,noise removal,social network,social networks,social network application,influence propagation model,social applications,marketing data processing,minimum cost flow model,network-flow based influence propagation model,social computing,propagates influence,social network services,information nodes,influence,social networking (online),negative information,information node,information propagation model,propagation model,social network marketing information,effective information propagation model,network flow | Flow network,Network science,Network formation,Dynamic network analysis,Organizational network analysis,Social network,Computer science,Network simulation,Minimum-cost flow problem,Management science,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-3027-5 | 12 | 0.58 |
References | Authors | |
21 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wookey Lee | 1 | 196 | 29.22 |
| Carson K. Leung | 2 | 1625 | 115.64 |
| Justin JongSu Song | 3 | 55 | 5.16 |
| Chris Soo-Hyun Eom | 4 | 31 | 3.41 |