Link Prediction Algorithm Based on Node Structure Similarity Measured by Relative Entropy

Yuyu, Meng; Guo, Jing

doi:10.1088/1742-6596/1955/1/012078

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The information entropy method can be used to quantify the link prediction problems based on a probability description. There are several entropy weight methods that have been proposed for link prediction research, such as the node similarity index of path entropy [38], structural entropy model [39], link prediction method based on relative entropy [40], and maximum entropy model [41]. The calculation process of information entropy is expressed as Equation (3).…”

Section: Information Entropymentioning

confidence: 99%

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

Zhang

Mao

et al. 2023

Symmetry

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Link prediction accuracy in temporal networks is easily affected by the time granularity of network snapshots. This is due to the insufficient information conveyed by snapshots and the lack of temporal continuity between snapshots. We propose a temporal network link prediction method based on the optimized exponential smoothing model and node interaction entropy (OESMNIE). This method utilizes fine-grained interaction information between nodes within snapshot periods and incorporates the information entropy theory to improve the construction of node similarity in the gravity model as well as the prediction process of node similarity. Experiment results on several real-world datasets demonstrate the superiority and reliability of this proposed method in adapting to link prediction requirements over other methods across different time granularities of snapshots, which is essential for studying the evolution of temporal networks.

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Section: Information Entropymentioning

confidence: 99%

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

Zhang

Mao

et al. 2023

Symmetry

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Bimodal accuracy distribution of link prediction in complex networks

Chen

Qian

Shen

et al. 2023

Int. J. Mod. Phys. C

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Link prediction plays an important role in information filtering and numerous research works have been made in this field. However, traditional link prediction algorithms mainly focus on overall prediction accuracy, ignoring the heterogeneity of the prediction accuracy for different links. In this paper, we analyzed the prediction accuracy of each link in networks and found that the prediction accuracy for different links is severely polarized. Further analysis shows that the accuracy of edges with low edge betweenness is consistently high while that of edges with high edge betweenness is consistently low, i.e. AUC follows a bimodal distribution with one peak around 0.5 and the other peak around 1. Our results indicate that link prediction algorithms should focus more on edges with high betweenness instead of edges with low betweenness. To improve the accuracy of edges with high betweenness, we proposed an improved algorithm called RA_LP which takes advantage of resource transfer of the second-order and third-order paths of local path. Results show that this algorithm can improve the link prediction accuracy for edges with high betweenness as well as the overall accuracy.

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Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours

2022

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A link prediction model for weighted networks based on Dempster–Shafer (DS) evidence theory and the influence of common neighbours is proposed in this paper. First, three types of future common neighbours (FCNs) and their topological structures are proposed. Second, the concepts of endpoint weight influence, link weight influence, and high-strength node influence are introduced. Then, the similarity based on the impacts of current common neighbours (CCNs) and FCNs is defined, respectively. Finally, the two similarity indices are fused by the DS evidence theory. This model effectively integrates multisource information and completely exploits the influence of all CCNs and FCNs on similarity. Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks.

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Link Prediction Algorithm Based on Node Structure Similarity Measured by Relative Entropy

Cited by 3 publications

References 18 publications

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy

Bimodal accuracy distribution of link prediction in complex networks

Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours

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