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 a
To measure the consistency between the clustering labels and reference labels, the adjusted Rand index (ARI), Fowlkes–Mallows index (FMI) and normalized mutual information (NMI) are employed to compare the performance of the different clustering algorithms (the higher the better) (Fig. 4a–c)....
& 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...
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...
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang WSDM 2018 Learning Structural Node Embeddings via Diffusion Wavelets Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec ...
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...
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...
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...
deep-learningtensorflowmachine-learning-algorithmspytorchdeepwalkdeep-learning-algorithmsnetwork-embeddingactive-learninggraph-convolutional-networksgcnnode2vecgraph-embeddinggraph-classificationactive-learning-modulenode-classificationgraph-neural-networksgraph-representation-learninggraph-convolutiongnngraph-neural-network ...
Ever since the development of high-throughput sequencing technologies, gene module detection methods have been a cornerstone for the biological interpretation of large gene compendia. Numerous approaches and algorithms have been proposed for the detection of gene modules through measuring gene expression3,...