Name
Abstract | ||
|---|---|---|
In social networks, several influential individuals can promote an idea or a product to numerous individuals. Thus, it is valuable to solve the influence maximization (IM) problem, which asks for finding the most influential set of individuals in a social network. To estimate the influence of individuals, the existing independent cascade (IC) model simulates the influence diffusion only considering the influences from direct in-neighbors to nodes. This consideration does not hold in real life. In many cases, people are likely influenced by information depending on where it comes from, instead of who gives it. To simulate the influence diffusion more accurate, this paper proposes the second-order IC model, which takes the previous influence into consideration. In addition, we design an approximate algorithm and its distributed extension for IM under the second-order IC model. Experimental results show that our second-order IC model outperforms the IC model in terms of simulating influence diffusions. The proposed algorithms are efficient, and the obtained node sets are influential. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSS.2019.2921422 | IEEE Transactions on Computational Social Systems |
Keywords | Field | DocType |
Integrated circuit modeling,Approximation algorithms,Computational modeling,Heuristic algorithms,Twitter | Data mining,Mathematical optimization,Social network,Computer science,Cascade,Maximization,Diffusion (business) | Journal |
Volume | Issue | ISSN |
6 | 4 | 2329-924X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenyi Tang | 1 | 1 | 0.70 |
| Guangchun Luo | 2 | 212 | 25.81 |
| Yubao Wu | 3 | 140 | 12.77 |
| Ling Tian | 4 | 34 | 8.67 |
| Xu Zheng | 5 | 206 | 14.74 |
| Zhipeng Cai | 6 | 1928 | 132.81 |