  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.
