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The Graph Neural Network Model

IEEE Transactions on Neural Networks, 2009
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural ...
SCARSELLI, FRANCO, GORI, MARCO, TSOI A. C, HAGENBUCHNER M, MONFARDINI, GABRIELE   +4 more
exaly   +9 more sources

Graph in Graph Neural Network

International Journal of Computer Vision
Abstract Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose vertices is represented by a vector or a single value, limited their representing capability to describe complex objects. In this paper, we propose a novel GNN (called Graph in Graph Neural (GIG) Network) which can process ...
Jiongshu Wang, Jing Yang 0038, Jiankang Deng, Hatice Gunes, Siyang Song   +4 more
semanticscholar   +5 more sources

Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

Defence Technology, 2023
With limited number of labeled samples, hyperspectral image (HSI) classification is a difficult Problem in current research. The graph neural network (GNN) has emerged as an approach to semi-supervised classification, and the application of GNN to ...
Ding Yao, Zhang Zhi-li, Zhao Xiao-feng, Cai Wei, He Fang, Cai Yao-ming, Wei-Wei Cai   +6 more
doaj   +3 more sources

Graph Neural Networks in Network Neuroscience

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph.
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik   +2 more
openaire   +6 more sources

Graph Neural Network Bandits [PDF]

Advances in Neural Information Processing Systems 35, 2022
We consider the bandit optimization problem with the reward function defined over graph-structured data. This problem has important applications in molecule design and drug discovery, where the reward is naturally invariant to graph permutations. The key challenges in this setting are scaling to large domains, and to graphs with many nodes.
Kassraie, Parnian, Krause, Andreas, Bogunovic, Ilija   +2 more
openaire   +5 more sources

Heterogeneous Graph Neural Network [PDF]

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the demand to incorporate heterogeneous structural (graph ...
Chuxu Zhang, Dongjin Song, Chao Huang 0001, Ananthram Swami, Nitesh V. Chawla   +4 more
openaire   +2 more sources

Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

Sensors, 2023
In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +4 more
doaj   +3 more sources

Motif Graph Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2023
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations.
Xuexin Chen, Ruichu Cai, Yuan Fang 0001, Min Wu 0008, Zijian Li 0001, Zhifeng Hao   +5 more
openaire   +5 more sources

Survey of Graph Neural Network [PDF]

Jisuanji gongcheng, 2021
With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
doaj   +1 more source

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [PDF]

AAAI Conference on Artificial Intelligence, 2021
Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains
Ailin Deng, Bryan Hooi
semanticscholar   +1 more source