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Nirmaljit Singh, Dr. Ikvinderpal Singh
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- Research Scholar, Assistant Professor, Department of Computer Science and Engineering, Sant Baba Bhag Singh University, Department of Computer Science and Applications, Trai Shatabdi GGS Khalsa College, Punjab, Punjab, India, India
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Abstract
nLink prediction in wireless networks plays a crucial role in predicting missing connections within multiplex networks. This study focuses on the utilization of similarity-based link prediction methods in wireless networks. These methods assume that the likelihood of linkage between nodes is determined by their similarity, based on shared features. Several similarity measures, such as Common Neighbors (CN), Preferential Attachment (PA), Adamic-Adar (AA), and Resource Allocation (RA) indices, are commonly employed to assess the structural similarity between nodes. These measures are favored because they offer a balance between computational efficiency and satisfactory predictive capabilities. Furthermore, the use of global similarity indices, such as the Katz index based on path length, incorporates information about the entire network structure and provides more accurate predictions. By employing these similarity- based methods, researchers and practitioners can gain valuable insights into the linkage patterns within wireless networks. This approach has been applied and evaluated in various multiplex networks, including social, biological, and technological networks. Commonly utilized for assessing the effectiveness of similarity-based link prediction methods are evaluation metrics such as the Area under the Receiver Operating Characteristic Curve (AUC) and precision.
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Keywords: link prediction, complex networks, Jaccard Index, preferential attachment, recommendation systems
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Wireless Security and Networks(ijwsn)]
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Full Text
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References
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| Volume | 01 |
| Issue | 02 |
| Received | September 14, 2023 |
| Accepted | September 22, 2023 |
| Published | September 25, 2023 |
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