Title |
Toward link predictability of complex networks
|
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Published in |
Proceedings of the National Academy of Sciences of the United States of America, February 2015
|
DOI | 10.1073/pnas.1424644112 |
Pubmed ID | |
Authors |
Linyuan Lü, Liming Pan, Tao Zhou, Yi-Cheng Zhang, H Eugene Stanley |
Abstract |
The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 30% |
Spain | 3 | 9% |
United Kingdom | 2 | 6% |
Sweden | 1 | 3% |
France | 1 | 3% |
Finland | 1 | 3% |
China | 1 | 3% |
Japan | 1 | 3% |
Italy | 1 | 3% |
Other | 0 | 0% |
Unknown | 12 | 36% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 19 | 58% |
Scientists | 13 | 39% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | <1% |
Portugal | 1 | <1% |
Singapore | 1 | <1% |
South Africa | 1 | <1% |
Unknown | 299 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 92 | 30% |
Student > Master | 42 | 14% |
Researcher | 26 | 9% |
Professor > Associate Professor | 16 | 5% |
Student > Bachelor | 14 | 5% |
Other | 55 | 18% |
Unknown | 59 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 92 | 30% |
Physics and Astronomy | 33 | 11% |
Agricultural and Biological Sciences | 21 | 7% |
Mathematics | 18 | 6% |
Engineering | 14 | 5% |
Other | 54 | 18% |
Unknown | 72 | 24% |