Optimized Influencers Profiling from Social Media Based on Machine Learning

Elbaghazaoui, Bahaa Eddine; Amnai, Mohamed; Fakhri, Yadolah

doi:10.1007/978-3-030-91738-8_5

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…) . (10) Theoretically, we are not need to rigorously limit the data distribution of the input variation as long as it is finite. As a result, we get 𝛿 2…”

Section: Feature Vector Using Dynamic Structured Convolutional Radial...mentioning

confidence: 99%

“…Regarding unsupervised techniques, 7 suggested leveraging node proximity and property data, and 8 used a hierarchical network method to forecast missing connections. Contrarily, supervised techniques have included supervised random walk algorithms that use labels to boost the likelihood of traversing established links, 9 while 10,11 extract features from outside sources as well as train methods on them to anticipate link development.…”

Section: Review Of Literaturementioning

confidence: 99%

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Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification

Sonti,

Mulpuri Santhi Sri,

Pamulapati

2024

Concurrency and Computation

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SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

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“…) . (10) Theoretically, we are not need to rigorously limit the data distribution of the input variation as long as it is finite. As a result, we get 𝛿 2…”

Section: Feature Vector Using Dynamic Structured Convolutional Radial...mentioning

confidence: 99%