Artificial Intelligence in Complex Networks

doi:10.3390/books978-3-7258-0934-9

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https://mdpi.com/books/pdfview/book/9159

Contributor(s)

Liu, Xiaoyang (editor)

Fiumara, Giacomo (editor)

De Meo, Pasquale (editor)

Ficara, Annamaria (editor)

Language

English

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Résumé

This Special Issue focuses on the modeling and analysis of network science, including that of key node identification, community detection, personalized recommendation systems, image processing, object detection, and the optimization method of artificial intelligence technology.

URI

https://directory.doabooks.org/handle/20.500.12854/137897

Keywords

complex network; distance distribution; multi-index fusion; relatively important node; infrared image; deep learning; neural network; target detection; transfer learning; multiscale characteristics; context analysis; temporal networks; community discovery; phylogenetic evolution; planted of partition; important nodes; relative importance; important edge; community detection; graph cut; betweenness centrality; modularity; social network; game theory; provider competition; 5G wireless production; equilibrium; social network analysis; bridge node detection; graph neural network; community; machine learning; system dynamics; simulation modeling; algorithmic decision-making; bounded rationality; supply chain planning; social relations; collaborative filtering; graph convolutional network; recommendation system; blockchain; Ethereum; cyberthreats; MLP; SVM; smart contract; directed network dismantling; non-backtracking matrix; spectral partition; minimal dismantling set; attention learning; tag information; tag-aware recommendation; e-health; decision function; complex background; MNSTLA; point target detection; multi-view gait recognition; Siamese neural network; vision transformer; view-feature conversion; gradual view; social recommendation; self-supervised learning; spherical fuzzy sets; objective weights; risk ranking; risk priority number; artificial intelligence; transformers; Vision Transformers (ViT); convolutional neural networks; multi-head attention; image classification; unsupervised learning; local node similarity; particle competition; stochastic competitive learning; complex networks; dynamic complex networks; opportunistic social mobility patterns; device-to-device data routing; spray and wait routing; quality of service