Artificial Intelligence in Complex Networks

© 2024 by the authors; CC BY-NC-ND license

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