Link prediction on signed social networks based on latent space mapping

Gu, Shensheng; Chen, Ling; Li, Bin; Liu, Wei; Chen, Bolun

doi:10.1007/s10489-018-1284-1

Cited by 8 publications

(2 citation statements)

References 42 publications

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“…Meanwhile, some considered taxonomies of link prediction techniques (Martínez, Berzal, & Cubero, 2017), (Al Hasan & Zaki, 2011) introduces four groups of approaches including similarity, statistical, algorithmic, and preprocessing approaches which can be applied to different networks including simple, social and complex ones. Most of the studies on the link prediction literature mainly focus on the unsigned networks like Facebook and MySpace; some research including (Gu, Chen, Li, Liu, & Chen, 2019) and (Yuan, He, Guan, Zhou, & Li, 2019) study the link prediction approach in signed networks. A detailed and comprehensive study of the state-of-the-art of link prediction approaches and a study on the computational complexity analysis of most important techniques are provided in (Martínez, Berzal, & Cubero, 2017).…”

Section: Related Workmentioning

confidence: 99%

Improving link prediction in social networks using local and global features: a clustering-based approach

Ghasemi

Zarei

2021

Prog Artif Intell

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Link prediction problem has increasingly become prominent in many domains such as social network analyses, bioinformatics experiments, transportation networks, criminal investigations and so forth. A variety of techniques has been developed for link prediction problem, categorized into 1) similarity based approaches which study a set of features to extract similar nodes; 2) learning based approaches which extract patterns from the input data; 3) probabilistic statistical approaches which optimize a set of parameters to establish a model which can best compute formation probability. However, existing literatures lack approaches which utilize strength of each approach by integrating them to achieve a much more productive one. To tackle the link prediction problem, we propose an approach based on the combination of first and second group methods; the existing studied works use just one of these categories. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes, which enforce the approach more efficiency compared to approaches using mere measures. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures which differentiate the strength of clusters; basically, the usage of local and global indices and the clustering information plays an imperative role in our link prediction process. Some extensive experiments held on real datasets including Facebook, Brightkite and HepTh indicate good performances of our proposal method. Besides, we have experimentally verified our approach with some previous techniques in the area to prove the supremacy of ours.

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Section: Related Workmentioning

confidence: 99%

Improving link prediction in social networks using local and global features: a clustering-based approach

Ghasemi

Zarei

2021

Prog Artif Intell

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“…Only recently have we witnessed an increase in the number of studies that discuss issues related to link prediction in signed networks. For example, [25] proposed a fresh approach with experimental results that show a high quality in terms of the accuracy and efficiency of link predictions.…”

Section: Predicting Links In Snmentioning

confidence: 99%

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Mohameds¹,

Zakis²,

Marjans³

2019

Adv. sci. technol. eng. syst. j.

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In more recent times, researchers have turned their attention to link prediction and the role link inference can play in better understanding the evolutionary nature of social networking sites. The objective of this paper is to present an in-depth review, analysis, and discussion of the cutting-edge link prediction methods that can be applied to better understand the development of social networks. The findings of the literature review reveal that there has been a steady increase in the number of published articles that present novel link prediction models that are designed to enhance the efficiency and accuracy of link prediction. In this paper, this most recent techniques to be proposed in this regard are compared and categorized, and features and evaluation metrics are presented for each approach. The results of the evaluation reveal that there are no complete or definitive methods available that can accurately and reliably be applied within different dynamic social networks to predict missing, emerging, and broken links within the network. The paper concludes by presenting potential future directions and recommendations for further studies

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Exploiting Modularity Maximisation in Signed Network Communities for Link Prediction

Abbasi

Muzammal

2022

Lecture Notes in Networks and Systems

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Link prediction on signed social networks based on latent space mapping

Cited by 8 publications

References 42 publications

Improving link prediction in social networks using local and global features: a clustering-based approach

Improving link prediction in social networks using local and global features: a clustering-based approach

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

Exploiting Modularity Maximisation in Signed Network Communities for Link Prediction

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