FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328007631> ?p ?o ?g. }

• W4328007631 endingPage "946" @default.
• W4328007631 startingPage "933" @default.
• W4328007631 abstract "Graph convolutional learning has led to many exciting discoveries in diverse areas. However, in some applications, traditional graphs are insufficient to capture the structure and intricacies of the data. In such scenarios, multigraphs arise naturally as discrete structures in which complex dynamics can be embedded. In this paper, we develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs). To capture the complex dynamics of information diffusion within and across each of the multigraph's classes of edges, we formalize a convolutional signal processing model, defining the notions of signals, filtering, and frequency representations on multigraphs. Leveraging this model, we develop a multigraph learning architecture, including a generalization of selection sampling to reduce computational complexity. The introduced architecture is applied towards optimal wireless resource allocation and a hate speech localization task, offering improved performance over traditional graph neural networks." @default.
• W4328007631 created "2023-03-22" @default.
• W4328007631 creator A5005778650 @default.
• W4328007631 creator A5021418299 @default.
• W4328007631 creator A5033202542 @default.
• W4328007631 date "2023-01-01" @default.
• W4328007631 modified "2023-10-10" @default.
• W4328007631 title "Convolutional Learning on Multigraphs" @default.
• W4328007631 cites W1987210641 @default.
• W4328007631 cites W1988263223 @default.
• W4328007631 cites W1998849268 @default.
• W4328007631 cites W2050766178 @default.
• W4328007631 cites W2100755644 @default.
• W4328007631 cites W2105633599 @default.
• W4328007631 cites W2112820013 @default.
• W4328007631 cites W2114010521 @default.
• W4328007631 cites W2118193919 @default.
• W4328007631 cites W2161763921 @default.
• W4328007631 cites W2179147795 @default.
• W4328007631 cites W2195972617 @default.
• W4328007631 cites W2215668387 @default.
• W4328007631 cites W2243638717 @default.
• W4328007631 cites W2296126341 @default.
• W4328007631 cites W2346122642 @default.
• W4328007631 cites W2473555522 @default.
• W4328007631 cites W2794875199 @default.
• W4328007631 cites W2796431263 @default.
• W4328007631 cites W2798598284 @default.
• W4328007631 cites W2808361044 @default.
• W4328007631 cites W2923035316 @default.
• W4328007631 cites W2943959761 @default.
• W4328007631 cites W2945266622 @default.
• W4328007631 cites W2959675886 @default.
• W4328007631 cites W2962779748 @default.
• W4328007631 cites W2965857891 @default.
• W4328007631 cites W2970120675 @default.
• W4328007631 cites W2970282997 @default.
• W4328007631 cites W2971598376 @default.
• W4328007631 cites W2972802819 @default.
• W4328007631 cites W2985331920 @default.
• W4328007631 cites W2992716901 @default.
• W4328007631 cites W3004507689 @default.
• W4328007631 cites W3011359269 @default.
• W4328007631 cites W3036615165 @default.
• W4328007631 cites W3083235778 @default.
• W4328007631 cites W3093080381 @default.
• W4328007631 cites W3097140498 @default.
• W4328007631 cites W3098856691 @default.
• W4328007631 cites W3113517187 @default.
• W4328007631 cites W3148274088 @default.
• W4328007631 cites W3153454506 @default.
• W4328007631 cites W3155957538 @default.
• W4328007631 cites W3166927129 @default.
• W4328007631 cites W3173543307 @default.
• W4328007631 cites W3177028972 @default.
• W4328007631 cites W3195962189 @default.
• W4328007631 cites W3209071166 @default.
• W4328007631 cites W4211021267 @default.
• W4328007631 cites W4290948206 @default.
• W4328007631 cites W4297849754 @default.
• W4328007631 cites W4306317391 @default.
• W4328007631 cites W595252221 @default.
• W4328007631 doi "https://doi.org/10.1109/tsp.2023.3259144" @default.
• W4328007631 hasPublicationYear "2023" @default.
• W4328007631 type Work @default.
• W4328007631 citedByCount "1" @default.
• W4328007631 crossrefType "journal-article" @default.
• W4328007631 hasAuthorship W4328007631A5005778650 @default.
• W4328007631 hasAuthorship W4328007631A5021418299 @default.
• W4328007631 hasAuthorship W4328007631A5033202542 @default.
• W4328007631 hasBestOaLocation W43280076312 @default.
• W4328007631 hasConcept C119857082 @default.
• W4328007631 hasConcept C132525143 @default.
• W4328007631 hasConcept C134306372 @default.
• W4328007631 hasConcept C154945302 @default.
• W4328007631 hasConcept C177148314 @default.
• W4328007631 hasConcept C17758045 @default.
• W4328007631 hasConcept C33923547 @default.
• W4328007631 hasConcept C41008148 @default.
• W4328007631 hasConcept C80444323 @default.
• W4328007631 hasConcept C81363708 @default.
• W4328007631 hasConceptScore W4328007631C119857082 @default.
• W4328007631 hasConceptScore W4328007631C132525143 @default.
• W4328007631 hasConceptScore W4328007631C134306372 @default.
• W4328007631 hasConceptScore W4328007631C154945302 @default.
• W4328007631 hasConceptScore W4328007631C177148314 @default.
• W4328007631 hasConceptScore W4328007631C17758045 @default.
• W4328007631 hasConceptScore W4328007631C33923547 @default.
• W4328007631 hasConceptScore W4328007631C41008148 @default.
• W4328007631 hasConceptScore W4328007631C80444323 @default.
• W4328007631 hasConceptScore W4328007631C81363708 @default.
• W4328007631 hasLocation W43280076311 @default.
• W4328007631 hasLocation W43280076312 @default.
• W4328007631 hasOpenAccess W4328007631 @default.
• W4328007631 hasPrimaryLocation W43280076311 @default.
• W4328007631 hasRelatedWork W2337926734 @default.
• W4328007631 hasRelatedWork W2978290780 @default.
• W4328007631 hasRelatedWork W3027997911 @default.

• next