Link prediction is the problem of predicting the location of either unknown or fake links from uncertain structural information of a network. Link prediction algorithms are useful in gaining insight into different network structures from partial observations of exemplars. However, existing link prediction algorithms only focus on regular complex networks and are overly dependent on either the closed triangular structure of networks or the so-called preferential attachment phenomenon. The performance of these algorithms on highly sparse or treelike networks is poor. In this letter, we proposed a method that is based on the network heterogeneity. We test our algorithms for three real large sparse networks: a metropolitan water distribution network, a Twitter network, and a sexual contact network. We find that our method is effective and performs better than traditional algorithms, especially for the Twitter network. We further argue that heterogeneity is the most obvious defining pattern for complex networks, while other statistical properties failed to be predicted. Moreover, preferential attachment based link prediction performed poorly and hence we infer that preferential attachment is not a plausible model for the genesis of many networks. We also suggest that heterogeneity is an important mechanism for online information propagation.
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Link prediction for tree-like networks
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June 2019
Research Article|
June 25 2019
Link prediction for tree-like networks
Ke-ke Shang
;
Ke-ke Shang
a)
1
Computational Communication Collaboratory, Nanjing University
, Nanjing 210093, People’s Republic of China
2
College of Big Data and Intelligent Engineering, Yangtze Normal University
, Chongqing 408100, People’s Republic of China
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Tong-chen Li;
Tong-chen Li
1
Computational Communication Collaboratory, Nanjing University
, Nanjing 210093, People’s Republic of China
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Michael Small
;
Michael Small
b)
3
Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia
, Crawley, Western Australia 6009, Australia
4
Mineral Resources, CSIRO
, Kensington, Western Australia 6151, Australia
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David Burton;
David Burton
5
Western Australia Water Corporation
, Leederville, Western Australia 6007, Australia
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Yan Wang
Yan Wang
1
Computational Communication Collaboratory, Nanjing University
, Nanjing 210093, People’s Republic of China
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a)
Electronic addresses: kekeshang@nju.edu.cn and keke.shang.1989@gmail.com
Chaos 29, 061103 (2019)
Article history
Received:
April 29 2019
Accepted:
May 23 2019
Citation
Ke-ke Shang, Tong-chen Li, Michael Small, David Burton, Yan Wang; Link prediction for tree-like networks. Chaos 1 June 2019; 29 (6): 061103. https://doi.org/10.1063/1.5107440
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