From raw detector activations to reconstructed particles, data at the Large Hadron Collider (LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural networks (GNNs), a class of algorithms belonging to the rapidly growing f
& Song, L. Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems 30 (2017).5.Kool, W., Van Hoof, H. & Welling, M. Attention, learn to solve routing problems! arXiv preprint...
Pitfalls of Graph Neural Network Evaluation Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann arXiv 2018 .11 Heterogeneous Graph Attention Network Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye WWW 2019 Bayesian graph convolutional neural netw...
validation and the software baseline. The average accuracy is 92.11%, comparable to state-of-the-art algorithms.f, The confusion matrices of the experimental classification results. The upper matrix is a ten-fold averaged confusion matrix, which is then normalized horizontally to produce the lower ...
To address this challenge, a natural idea is to properly simplify, or reduce the graph so that we can not only speed up graph algorithms (including GNNs) but also facilitate storage, visualization and retrieval for associated graph data analysis tasks. 然而,现实场景中大规模图的流行,通常在数...
决策优化 组合优化 GCN structure2vec 《Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search》《 Learning combinatorial optimization algorithms over graphs》 NPHard 交通 出租车需求预测 《Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction》 DMVST-Net 交通 交通流...
In fact, before the rise of deep learning, the industry has already begun to explore the technology of Graph Embedding[1]. The early graph embedding algorithms were mostly based on heuristic matrix decomposition and probabilistic graph models; later, more "shallow" neural network models represented...
4.Khalil, E., Dai, H., Zhang, Y., Dilkina, B. & Song, L. Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems 30 (2017). 5.Kool, W., Van Hoof, H. & Welling, M. Attention, learn to solve routing problems! arXiv preprint arXi...
Since the service requirements are highly dynamic in IIoT systems, the network function virtualization technology receives growing attention. Given limited computing resources in the virtual environment, designing efficient scheduling algorithms is necessary. To this end, the proposed DDQ leverages GNN to ...
deep-learningtensorflowmachine-learning-algorithmspytorchdeepwalkdeep-learning-algorithmsnetwork-embeddingactive-learninggraph-convolutional-networksgcnnode2vecgraph-embeddinggraph-classificationactive-learning-modulenode-classificationgraph-neural-networksgraph-representation-learninggraph-convolutiongnngraph-neural-network ...