Results 21 to 30 of about 24,295 (253)
Auto-GNN: Neural architecture search of graph neural networks
Frontiers in Big Data, 2022 Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As the graph characteristics vary significantly in real-world systems, given a specific scenario, the architecture parameters need to be tuned carefully to identify a suitable GNN.
Kaixiong Zhou +4 more
openaire +4 more sources
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
Frontiers in Neurorobotics, 2021 The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered.
Shuhao Shi +5 more
doaj +1 more source
IEEE Access, 2021 Graph-based methods have been widely used by the document image analysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation
Nadeem Iqbal Kajla +3 more
doaj +1 more source
Edge Ranking of Graphs in Transportation Networks using a Graph Neural Network (GNN)
CoRR, 2023 Many networks, such as transportation, power, and water distribution, can be represented as graphs. Crucial challenge in graph representations is identifying the importance of graph edges and their influence on overall network efficiency and information flow performance.
Debasish Jana +3 more
openaire +2 more sources
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion [PDF]
Hydrology and Earth System Sciences, 2022 Rivers and river habitats around the world are under sustained pressure from human activities and the changing global environment. Our ability to quantify and manage the river states in a timely manner is critical for protecting the public safety and ...
A. Y. Sun +5 more
doaj +1 more source
Graph Neural Network Defense Combined with Contrastive Learning [PDF]
Jisuanji kexue yu tansuo, 2023 Although graph neural networks have achieved good performance in the field of graph representation learning, recent studies have shown that graph neural networks are more susceptible to adversarial attacks on graph structure, namely, by adding well ...
CHEN Na, HUANG Jincheng, LI Ping
doaj +1 more source
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 Published on KDD ...
Ziniu Hu +4 more
openaire +2 more sources
Node Classification on The Citation Network Using Graph Neural Network
Inspiration, 2023 Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features.
Irani Hoeronis +1 more
doaj +1 more source
Research on Node Learning of Graph Neural Networks Fusing Positional and StructuralInformation [PDF]
Jisuanji kexueGraph neural networks are powerful models for learning graph-structured data,representing them through node information embedding and graph convolution operations.In graph data,the structural information and positional information of nodes are crucial ...
HAO Jiahui, WAN Yuan, ZHANG Yuhang
doaj +1 more source
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective [PDF]
, 2020 Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in ...
Avelar, Pedro +5 more
core +2 more sources