Open AccessLink Prediction Based on the Derivation of Mapping Entropy
Author(s) -
Hefei Hu,
Yanan Wang,
Zheng Li,
Yang Tian,
Yi Ren
Publication year - 2021
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1155/2021/4156832
Subject(s) - computer science , link (geometry) , node (physics) , entropy (arrow of time) , similarity (geometry) , data mining , theoretical computer science , algorithm , artificial intelligence , image (mathematics) , computer network , physics , structural engineering , quantum mechanics , engineering
The algorithms based on topological similarity play an important role in link prediction. However, most of traditional algorithms based on the influences of nodes only consider the degrees of the endpoints which ignore the differences in contribution of neighbors. Through generous explorations, we propose the DME (derivation of mapping entropy) model concerning the mapping relationship between the node and its neighbors to access the influence of the node appropriately. Abundant experiments on nine real networks suggest that the model can improve precision in link prediction and perform better than traditional algorithms obviously with no increase in time complexity.