Name
Abstract | ||
|---|---|---|
The fast growth of social networks and their privacy requirements in recent years, has lead to increasing difficulty in obtaining complete topology of these networks. However, diffusion information over these networks is available and many algorithms have been proposed to infer the underlying networks by using this information. The previously proposed algorithms only focus on inferring more links and do not pay attention to the important characteristics of the underlying social networks In this paper, we propose a novel algorithm, called DANI, to infer the underlying network structure while preserving its properties by using the diffusion information. Moreover, the running time of the proposed method is considerably lower than the previous methods. We applied the proposed method to both real and synthetic networks. The experimental results showed that DANI has higher accuracy and lower run time compared to well-known network inference methods. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Social and Information Networks | Social network,Computer science,Inference,Algorithm,Artificial intelligence,Machine learning,Network structure |
DocType | Volume | Citations |
Journal | abs/1706.00941 | 0 |
PageRank | References | Authors |
0.34 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Ramezani | 1 | 0 | 0.34 |
| Hamid R. Rabiee | 2 | 336 | 41.77 |
| Maryam Tahani | 3 | 0 | 0.34 |
| Arezoo Rajabi | 4 | 2 | 1.73 |