Abstract
The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity.
Highlights
The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors
In the past few years, many link prediction algorithms have been proposed, but most of the algorithms do not account for missing links between two nodes with no common neighbors
To predict these missing links between nodes with no common neighbors are very difficult because they account for low proportion in missing links, but they have significance in determining network structure and network properties
Summary
The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of realworld networks without increasing complexity. A part of missing links could not be predicted because there are no common neighbors between them, but they often play a key role to connect different communities, and affect network properties, such as betweenness centrality, average distance, congestion and spreading ability. It is important to propose an algorithm to predict missing links between nodes with no common neighbors. The experimental results show that it can obtain significantly better prediction accuracy for a variety of real-world networks than other methods
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