Reprint

Artificial Intelligence in Complex Networks

Edited by
April 2024
366 pages
  • ISBN978-3-7258-0933-2 (Hardback)
  • ISBN978-3-7258-0934-9 (PDF)

This is a Reprint of the Special Issue Artificial Intelligence in Complex Networks that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

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.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
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; complex network; 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; machine learning; MLP; SVM; smart contract; directed network dismantling; non-backtracking matrix; spectral partition; minimal dismantling set; attention learning; tag information; tag-aware recommendation; blockchain; e-health; machine learning; deep learning; smart contract; decision function; complex background; infrared image; MNSTLA; point target detection; multi-view gait recognition; Siamese neural network; vision transformer; view-feature conversion; gradual view; social recommendation; self-supervised learning; graph convolutional network; 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; community detection; 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

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