Link prediction based on a semi-local similarity index

白萌,; 胡柯,; 唐翌,

doi:10.1088/1674-1056/20/12/128902

Cited by 34 publications

(12 citation statements)

References 32 publications

(41 reference statements)

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“…Zhou et al [35] extend some similarity index (CN, AA and RA index) from binary networks to weighted networks according to Murata and Moriyasu's reach [36]. And Bai et al first developed the weighted LP index [37]. The four weighted similarity indices are summarized as below.…”

Section: Enhance the Robustnessmentioning

confidence: 99%

Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks

Wang¹,

Hu²,

Tang³

2014

CBIO

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Many algorithms have been proposed to predict missing links in a variety of real networks. These studies focus on mainly both accuracy and efficiency of these algorithms. However, little attention is paid to their robustness against either noise or irrationality of a link existing in almost all of real networks.In this paper, we investigate the robustness of several typical node-similarity-based algorithms and find that these algorithms are sensitive to the strength of noise. Moreover, we find that it also depends on network's structure properties, especially on network efficiency, clustering coefficient and average degree. In addition, we make an attempt to enhance the robustness by using link weighting method to transform un-weighted network to weighted one and then make use of weights of links to characterize their reliability. The result shows that proper link weighting scheme can enhance both robustness and accuracy of these algorithms significantly in biological networks while it brings little computational effort.

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Section: Enhance the Robustnessmentioning

confidence: 99%

Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks

Wang¹,

Hu²,

Tang³

2014

CBIO

Self Cite

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“…An attractive research topic is link prediction, whose purpose is to predict the possibility or necessity of forming links between unconnected node pairs via the information of complex networks 1 , 2 . Thus Link prediction can predict the existing yet unknown links (the missing links) and the links that may appear in the future (the future links) 3 , 4 . With the amount of data increasing nowadays, Link prediction plays a more crucial role in recommendation system, data mining, complex networks, and so on.…”

Section: Introductionmentioning

confidence: 99%

Similarity-based future common neighbors model for link prediction in complex networks

Huang

Zhang

et al. 2018

Sci Rep

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Link prediction aims to predict the existence of unknown links via the network information. However, most similarity-based algorithms only utilize the current common neighbor information and cannot get high enough prediction accuracy in evolving networks. So this paper firstly defines the future common neighbors that can turn into the common neighbors in the future. To analyse whether the future common neighbors contribute to the current link prediction, we propose the similarity-based future common neighbors (SFCN) model for link prediction, which accurately locate all the future common neighbors besides the current common neighbors in networks and effectively measure their contributions. We also design and observe three MATLAB simulation experiments. The first experiment, which adjusts two parameter weights in the SFCN model, reveals that the future common neighbors make more contributions than the current common neighbors in complex networks. And two more experiments, which compares the SFCN model with eight algorithms in five networks, demonstrate that the SFCN model has higher accuracy and better performance robustness.

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“…Murata and Moriyasu [33] improved the CN and AA indices by using the link weights information. Bai Meng etc [34] developed the weighted version of LP index. Lü L Y etc.…”

Section: Introductionmentioning

confidence: 99%

“…where O ab is the set of common neighbors of node a and b, and Γ(c) is the set of neighbors of node c. A is the adjacency matrix, and we set ǫ = 0.01 to obtain a near optimal prediction accuracy. In addition, their parameterdependent weighted versions, WCN [33], WAA [33], and WLP [34] are respectively defined as follows:…”

mentioning

confidence: 99%

Entropy-based link prediction in weighted networks

Sharafat

et al. 2017

Chinese Phys. B

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Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 [31]), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.

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Link prediction based on a semi-local similarity index

Cited by 34 publications

References 32 publications

Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks

Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks

Similarity-based future common neighbors model for link prediction in complex networks

Entropy-based link prediction in weighted networks

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