Results 31 to 40 of about 4,082,283 (306)

Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks

IEEE Signal Processing Magazine, 2020
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure.
Fernando Gama, Elvin Isufi, Geert Leus, Alejandro Ribeiro   +3 more
openaire   +4 more sources

Graph Condensation for Graph Neural Networks

CoRR, 2021
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and
Wei Jin 0009, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah   +5 more
openaire   +4 more sources

Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]

Jisuanji kexue
Node classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
doaj   +1 more source

Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]

, 2001
Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán, Molina López, José Manuel, Galván, Inés M., Sanchis de Miguel, María Araceli, Isasi, Pedro, Galván León, Inés María, Isasi Viñuela, Pedro   +6 more
core   +1 more source

Research Progress of Graph Neural Network Using Edge Information [PDF]

Jisuanji kexue yu tansuo
Graph neural network is a deep learning technique that can process non-Euclidean structured complex data which can be transformed into graphs. In recent years, it has received a lot of research attention, providing a new research perspective and powerful
ZHU Tao, GAO Guangliang, WANG Qun, XIA Lingling, LIANG Guangjun, MA Zhuo
doaj   +1 more source

Graph Summarization with Graph Neural Networks

CoRR, 2022
The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class.
Maximilian Blasi, Manuel Freudenreich, Johannes Horvath, David Richerby, Ansgar Scherp   +4 more
openaire   +3 more sources

Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

Frontiers in Artificial Intelligence, 2021
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel.
Paul Y. Wang, Paul Y. Wang, Sandalika Sapra, Sandalika Sapra, Vivek Kurien George, Vivek Kurien George, Gabriel A. Silva, Gabriel A. Silva, Gabriel A. Silva   +8 more
doaj   +1 more source

Benchmarking Graph Neural Networks

J. Mach. Learn. Res., 2020
Benchmarking framework on GitHub at https://github.com/graphdeeplearning/benchmarking ...
Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent 0001, Yoshua Bengio, Xavier Bresson   +5 more
openaire   +4 more sources

Review of Graph Neural Networks [PDF]

Jisuanji kexue
With the rapid development of artificial intelligence,deep learning has achieved great success in data that can be represented in Euclidean spaces,such as images,text,and speech.However,it has been difficult to apply deep learning to non-Eucli-dean ...
HOU Lei, LIU Jinhuan, YU Xu, DU Junwei
doaj   +1 more source

Studying the capacity of cellular encoding to generate feedforward neural network topologies [PDF]

, 2004
Proceeding of: IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, 25-29 July 2004Many methods to codify artificial neural networks have been developed to avoid the disadvantages of direct encoding schema, improving the search ...
Gutiérrez Sánchez, Germán, Galván, Inés M., Molina, José M., Sanchis de Miguel, María Araceli   +3 more
core   +1 more source