
Predicting missing links in complex networks based on common neighbors and distance
Author(s) -
Jiancheng Yang,
Xiaodong Zhang
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep38208
Subject(s) - computer science , metric (unit) , data mining , complex network , missing data , variety (cybernetics) , artificial intelligence , machine learning , operations management , world wide web , economics
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.