Name
Abstract | ||
|---|---|---|
With the wide usage of many electronic devices such as the mobile telephones, the social activities in the mobile terminal have increased greatly. The influence maximization problem is to find a small set of nodes in a mobile social network such that they can make to maximize the spread of influence under the certain models. Existing methods of influence maximization only consider information dissemination, without considering the effectiveness of the transmission probabilities between nodes. We need to ensure the effectiveness of the transmission probabilities to achieve the maximal influence in real applications. We design a new propagation model and propose an effective greedy algorithm for this model. On this basis, we further improve the algorithmic efficiency with dynamic pruning strategy and propose a new estimation technique to accelerate influence evaluation. We demonstrate the effectiveness and efficiency of our algorithms on the two real social networks. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MSN.2018.00017 | 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) |
Keywords | Field | DocType |
social relationship, user similarity, dynamic pruning strategy | Algorithmic efficiency,Social network,Mobile social network,Computer science,Greedy algorithm,Electronics,Information Dissemination,Small set,Maximization,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-7281-0548-2 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Wang | 1 | 93 | 27.47 |
| Jiansong Da | 2 | 1 | 0.35 |
| Jinbao Li | 3 | 251 | 39.56 |
| Yong Liu | 4 | 1 | 0.35 |