DANI: A Fast Diffusion Aware Network Inference Algorithm

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.

References in corpus (8)

• Finding community structure in networks using the eigenvectors of matrices
• Near linear time algorithm to detect community structures in large-scale networks
• Benchmark graphs for testing community detection algorithms
• Detecting the overlapping and hierarchical community structure of complex networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• A Poissonian explanation for heavy-tails in e-mail communication
• Towards real-time community detection in large networks
• Prisoner's Dilemma on community networks