Matches in SemOpenAlex for { <https://semopenalex.org/work/W4212852078> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4212852078 abstract "Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning." @default.
- W4212852078 created "2022-02-24" @default.
- W4212852078 creator A5012502919 @default.
- W4212852078 creator A5013881064 @default.
- W4212852078 creator A5029588473 @default.
- W4212852078 creator A5044455276 @default.
- W4212852078 date "2022-02-11" @default.
- W4212852078 modified "2023-10-14" @default.
- W4212852078 title "Graph Minimally-supervised Learning" @default.
- W4212852078 cites W2022322548 @default.
- W4212852078 cites W2759136286 @default.
- W4212852078 cites W2803831897 @default.
- W4212852078 cites W2808409763 @default.
- W4212852078 cites W2906836970 @default.
- W4212852078 cites W2944250323 @default.
- W4212852078 cites W2964051675 @default.
- W4212852078 cites W2984323660 @default.
- W4212852078 cites W2991221721 @default.
- W4212852078 cites W2997198750 @default.
- W4212852078 cites W2997262687 @default.
- W4212852078 cites W2997738974 @default.
- W4212852078 cites W3034213836 @default.
- W4212852078 cites W3080997787 @default.
- W4212852078 cites W3086452730 @default.
- W4212852078 cites W3093957844 @default.
- W4212852078 cites W3094624443 @default.
- W4212852078 cites W3099152386 @default.
- W4212852078 cites W3099378125 @default.
- W4212852078 cites W3106229813 @default.
- W4212852078 cites W3130274530 @default.
- W4212852078 cites W3152507776 @default.
- W4212852078 cites W3173421061 @default.
- W4212852078 cites W3210137232 @default.
- W4212852078 cites W4225657277 @default.
- W4212852078 doi "https://doi.org/10.1145/3488560.3501390" @default.
- W4212852078 hasPublicationYear "2022" @default.
- W4212852078 type Work @default.
- W4212852078 citedByCount "2" @default.
- W4212852078 countsByYear W42128520782023 @default.
- W4212852078 crossrefType "proceedings-article" @default.
- W4212852078 hasAuthorship W4212852078A5012502919 @default.
- W4212852078 hasAuthorship W4212852078A5013881064 @default.
- W4212852078 hasAuthorship W4212852078A5029588473 @default.
- W4212852078 hasAuthorship W4212852078A5044455276 @default.
- W4212852078 hasConcept C108583219 @default.
- W4212852078 hasConcept C119857082 @default.
- W4212852078 hasConcept C132525143 @default.
- W4212852078 hasConcept C154945302 @default.
- W4212852078 hasConcept C2522767166 @default.
- W4212852078 hasConcept C41008148 @default.
- W4212852078 hasConcept C58973888 @default.
- W4212852078 hasConcept C80444323 @default.
- W4212852078 hasConcept C94124525 @default.
- W4212852078 hasConceptScore W4212852078C108583219 @default.
- W4212852078 hasConceptScore W4212852078C119857082 @default.
- W4212852078 hasConceptScore W4212852078C132525143 @default.
- W4212852078 hasConceptScore W4212852078C154945302 @default.
- W4212852078 hasConceptScore W4212852078C2522767166 @default.
- W4212852078 hasConceptScore W4212852078C41008148 @default.
- W4212852078 hasConceptScore W4212852078C58973888 @default.
- W4212852078 hasConceptScore W4212852078C80444323 @default.
- W4212852078 hasConceptScore W4212852078C94124525 @default.
- W4212852078 hasFunder F4320337345 @default.
- W4212852078 hasFunder F4320338281 @default.
- W4212852078 hasLocation W42128520781 @default.
- W4212852078 hasOpenAccess W4212852078 @default.
- W4212852078 hasPrimaryLocation W42128520781 @default.
- W4212852078 hasRelatedWork W2597787948 @default.
- W4212852078 hasRelatedWork W2795261237 @default.
- W4212852078 hasRelatedWork W3014300295 @default.
- W4212852078 hasRelatedWork W4223943233 @default.
- W4212852078 hasRelatedWork W4225161397 @default.
- W4212852078 hasRelatedWork W4312200629 @default.
- W4212852078 hasRelatedWork W4360585206 @default.
- W4212852078 hasRelatedWork W4364306694 @default.
- W4212852078 hasRelatedWork W4380075502 @default.
- W4212852078 hasRelatedWork W4380086463 @default.
- W4212852078 isParatext "false" @default.
- W4212852078 isRetracted "false" @default.
- W4212852078 workType "article" @default.