Link prediction based on the powerful combination of endpoints and neighbors

Gao, Tianrun; Zhu, Xuzhen

doi:10.1142/s0217979220502690

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The basic node centralities and various binary machine learning classifiers are used to predict links. T Gao et al's [25] focus was on degrees of end points and neighbors, so the authors proposed a powerful combination of endpoints and neighbors (PCEN) model, which gets better prediction results than existing models. Kumar et al [26] proposed a new approach to link prediction based on the level-2 node clustering coefficient.…”

Section: Recent Workmentioning

confidence: 99%

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score

Nandini,

Lakshmi,

Enduri

et al. 2024

Entropy

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Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic–Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.

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Section: Recent Workmentioning

confidence: 99%

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score

Nandini,

Lakshmi,

Enduri

et al. 2024

Entropy

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SCL-WTNS: A new link prediction algorithm based on strength of community link and weighted two-level neighborhood similarity

Zhou

Peng

et al. 2022

Int. J. Mod. Phys. B

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Link prediction is a significant and fundamental research issue in the field of network science. Numerous similarity-based algorithms have been widely applied due to low computational cost and high prediction accuracy. The topological features of networks like community structure are beneficial to link prediction. In this paper, we first introduce a Weighted Two-level Neighborhood Similarity (WTNS) index that integrates the resource allocation index and local path index. Then we define the Strength of Community Link (SCL) as a quantitative index to evaluate the close relationship among communities. Based on this, the connection likelihood between two nodes can be calculated and a new link prediction algorithm called SCL-WTNS is presented. The proposed algorithm has been compared with nine popular similarity methods on 12 real-world networks to verify the performance. SCL-WTNS is also compared with two groups of community-based link prediction methods. Experiments indicate that the proposed algorithm is better than comparison algorithms both in prediction accuracy and stability.

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Modeling an web community discovery method with web page attraction

Shi

2021

IFS

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An improved Web community discovery algorithm is proposed in this paper based on the attraction between Web pages to effectively reduce the complexity of Web community discovery. The proposed algorithm treats each Web page in the Web pages collection as an individual with attraction based on the theory of universal gravitation, elaborates the discovery and evolution process of Web community from a Web page in the Web pages collection, defines the priority rules of Web community size and Web page similarity, and gives the calculation formula of the change in Web page similarity. Finally, an experimental platform is built to analyze the specific discovery process of the Web community in detail, and the changes in cumulative distribution of Web page similarity are discussed. The results show that the change in the similarity of a new page satisfies the power-law distribution, and the similarity of a new page is proportional to the size of Web community that the new page chooses to join.

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Link prediction based on the powerful combination of endpoints and neighbors

Cited by 3 publications

References 32 publications

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score

SCL-WTNS: A new link prediction algorithm based on strength of community link and weighted two-level neighborhood similarity

Modeling an web community discovery method with web page attraction

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