Thorpe M, Nguyen TM, Xia H, et al (2022) GRAND++: Graph neural diffusion with a source term. In: International conference on learning representations. https://openreview.net/forum?id=EMxu-dzvJk Topping J, Di Giovanni F, Chamberlain BP, et al (2021) Understanding over-squashing and bott...
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. NIPS 2016. paper Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst. Diffusion-Convolutional Neural Networks. NIPS 2016. paper James Atwood, Don Towsley. Gated Graph Sequence Neural Networks. ICLR 2016. paper Yujia...
Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground...
We conduct a comprehensive comparison of GCPAL with a variety of established baseline methods. GNN-based methods: GCN [53] is a neural network architecture that performs convolutional operations on graph-structured data, using localized node features and graph topology to learn node embeddings. GAT ...
The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.Similar content being viewed by others Network cartographs for interpretable visualizations Article Open access 24 February 2022 ...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network[J]. Applied Sciences, 2023, 13(8): 4910. Link Fafoutellis P, Vlahogianni E I. Traffic Demand Prediction Using a Social Multiplex Networks Representation on a Multimodal and Multisource Dataset...
They are commonly used in link prediction as Auto-Encoders are good at dealing with class balance. Recurrent Graph Neural Networks(RGNNs) learn the best diffusion pattern, and they can handle multi-relational graphs where a single node has multiple relations. This type of graph neural network...
Based on the hyperbolic graph neural network, dependent syntactic information and information optimization strategies are introduced to solve the problem of word embedding concentration. Simultaneously, to mitigate the impact of noise in dependency syntax information on the relation extraction task, a ...
Compared with traditional deep neural networks, an ordinary GCN or a spatiotemporal GCN model significantly improves the resulting recognition accuracy. However, according to our analysis, the current mainstream models face at least the following two challenges. 1) The intraindividual differences among ...