Link prediction algorithm based on local centrality of common neighbor nodes using multi-attribute ranking

Mingqiang, Zhou; Liu, Rongchen; Zhao, Xin; Zhu, Qingsheng

doi:10.1109/iccse.2017.8085544

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…The work in [39] also adopted TOPSIS in link prediction; however, it is totally different from our work. In [39], TOPSIS is only used to evaluate the local centralities of common neighbors. The similarity score between two nodes is computed based on the local centralities of their common neighbors.…”

Section: Introductionmentioning

confidence: 87%

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Bai

Leng

et al. 2018

Complexity

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Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 87%

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Bai

Leng

et al. 2018

Complexity

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“…The RA index is the same as the AA index, with the exception that the weight value of the RA index is equal to the reciprocal of the node degree, which is defined in equation (6)…”

Section: Based On Local Information Similarity Indicesmentioning

confidence: 99%

“…The simplest index is CN 6,7 which means common neighbor. If there are many common neighbors between nodes x and y, the nodes x and y are similar, and the CN index is defined in equation ( 1)…”

Section: Based On Local Information Similarity Indicesmentioning

confidence: 99%

Study on similarity indices for link prediction in opportunistic networks

Cai

Shu

Liu

2018

Advances in Mechanical Engineering

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Link prediction aims to estimate the existence of links between nodes, using information of network structures and node properties. According to the characteristics of node mobility, node intermittent contact, and high delay of opportunistic network, novel similarity indices are constructed based on CN, AA, and RA. The indices CN, AA, and RA do not consider the historic information of networks. Similarity indices, T_CN, T_AA, and T_RA, based on temporal characteristics are proposed. These take the historic information of network evolution into consideration. Using historic information of the evolution of opportunistic networks and 2-hop neighbor information of the nodes, similarity indices based on the temporal-spatial characteristics, O_CN, O_AA, and O_RA, are proposed. Based on the imote traces cambridge (ITC) and detected social network (DSN) datasets, the experimental results indicate that similarity indices O_CN, O_AA, and O_RA outperform CN, AA, and RA. Furthermore, index O_AA has superior performance.

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Link Prediction Approach for Opportunistic Networks Based on Recurrent Neural Network

2019

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Link prediction algorithm based on local centrality of common neighbor nodes using multi-attribute ranking

Cited by 4 publications

References 12 publications

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Study on similarity indices for link prediction in opportunistic networks

Link Prediction Approach for Opportunistic Networks Based on Recurrent Neural Network

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