A review of similarity measures and link prediction models in social networks

al., S Hemkiran et.

doi:10.12785/ijcds/090209

Cited by 3 publications

References 50 publications

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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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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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A Review of Extracting and Mining User Interest from Social Media Based on Personality

Alrehili

Yafooz

2021

2021 3rd International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

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The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

et al. 2022

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The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

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A review of similarity measures and link prediction models in social networks

Cited by 3 publications

References 50 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

A Review of Extracting and Mining User Interest from Social Media Based on Personality

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

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