Results 11 to 20 of about 142,397 (310)
Transfer Entropy in Graph Convolutional Neural Networks [PDF]
2024 28th International Conference Information Visualisation (IV)Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In contrast to Convolutional Neural Networks, GCN's are designed to perform inference on graphs, where the number of nodes can vary, and the nodes are unordered.
Adrian Moldovan +2 more
openalex +3 more sources
Neighborhood Convolutional Graph Neural Network
Knowledge-Based Systems, 2023 Jinsong Chen, Boyu Li, Kun He
openalex +2 more sources
Multiscale Global Adaptive Attention Graph Neural Network [PDF]
Jisuanji kexue yu tansuo, 2023 Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields.
GOU Ruru, YANG Wenzhu, LUO Zifei, YUAN Yunfeng
doaj +1 more source
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 +3 more
+9 more sources
SPATIOTEMPORAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR METRO FLOW PREDICTION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Forecasting urban metro flow accurately plays an important role for station management and passenger safety. Owing to the limitations of non-linearity and complexity of traffic flow data, traditional methods cannot satisfy the requirements of effectively
S. Jin, C. Jing, Y. Wang, X. Lv
doaj +1 more source
Applied Sciences, 2022 Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as ...
Ji-Hun Bae +6 more
doaj +1 more source
Review of Text Classification Methods Based on Graph Convolutional Network [PDF]
Jisuanji kexue, 2022 Text classification is a common task in natural language processing,in which there are a lot of research and progress based on machine learning and deep learning.However,these traditional methods can only process Euclidean spatial data,and cannot express
TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo
doaj +1 more source
Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
openaire +4 more sources
Graph Neural Networks with Convolutional ARMA Filters [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better ...
Filippo Maria Bianchi +3 more
openaire +3 more sources
Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 more
doaj +1 more source