Link Prediction in Evolving Networks Based on Popularity of Nodes

Abstract Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based...

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Autores principales:	Tong Wang, Xing-Sheng He, Ming-Yang Zhou, Zhong-Qian Fu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5da37530ac794be1b2308126671d49c2
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spelling	oai:doaj.org-article:5da37530ac794be1b2308126671d49c22021-12-02T16:06:46ZLink Prediction in Evolving Networks Based on Popularity of Nodes10.1038/s41598-017-07315-42045-2322https://doaj.org/article/5da37530ac794be1b2308126671d49c22017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07315-4https://doaj.org/toc/2045-2322Abstract Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.Tong WangXing-Sheng HeMing-Yang ZhouZhong-Qian FuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tong Wang Xing-Sheng He Ming-Yang Zhou Zhong-Qian Fu Link Prediction in Evolving Networks Based on Popularity of Nodes
description	Abstract Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.
format	article
author	Tong Wang Xing-Sheng He Ming-Yang Zhou Zhong-Qian Fu
author_facet	Tong Wang Xing-Sheng He Ming-Yang Zhou Zhong-Qian Fu
author_sort	Tong Wang
title	Link Prediction in Evolving Networks Based on Popularity of Nodes
title_short	Link Prediction in Evolving Networks Based on Popularity of Nodes
title_full	Link Prediction in Evolving Networks Based on Popularity of Nodes
title_fullStr	Link Prediction in Evolving Networks Based on Popularity of Nodes
title_full_unstemmed	Link Prediction in Evolving Networks Based on Popularity of Nodes
title_sort	link prediction in evolving networks based on popularity of nodes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/5da37530ac794be1b2308126671d49c2
work_keys_str_mv	AT tongwang linkpredictioninevolvingnetworksbasedonpopularityofnodes AT xingshenghe linkpredictioninevolvingnetworksbasedonpopularityofnodes AT mingyangzhou linkpredictioninevolvingnetworksbasedonpopularityofnodes AT zhongqianfu linkpredictioninevolvingnetworksbasedonpopularityofnodes
_version_	1718384843058839552

Link Prediction in Evolving Networks Based on Popularity of Nodes

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