Link prediction based on contribution of neighbors
| Autor: | Xiang-Chun Liu, Xuzhen Zhu, Dian-Qing Meng, Yang Tian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Node (networking) General Physics and Astronomy Statistical and Nonlinear Physics Link (geometry) Complex network Random walk computer.software_genre 01 natural sciences 010305 fluids & plasmas Computer Science Applications Computational Theory and Mathematics Similarity (network science) Prediction methods 0103 physical sciences Data mining 010306 general physics computer Mathematical Physics |
| Zdroj: | International Journal of Modern Physics C. 31:2050158 |
| ISSN: | 1793-6586 0129-1831 |
| DOI: | 10.1142/s0129183120501582 |
| Popis: | Link prediction based on node similarity has become one of the most effective prediction methods for complex network. When calculating the similarity between two unconnected endpoints in link prediction, most scholars evaluate the influence of endpoint based on the node degree. However, this method ignores the difference in contribution of neighbor (NC) nodes for endpoint. Through abundant investigations and analyses, the paper quantifies the NC nodes to endpoint, and conceives NC Index to evaluate the endpoint influence accurately. Extensive experiments on 12 real datasets indicate that our proposed algorithm can increase the accuracy of link prediction significantly and show an obvious advantage over traditional algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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