Here we propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs. We derive GCNs operations in the hyperboloid ...
Graph convolutional neural networks (GCNs) map nodes in a graph to Euclidean embeddings, which have been shown to incur a large distortion (导致变形) when embedding real-world graphs with scale-free or hierarchical structure. Hyperbolic geometry offers an exciting alternative, as it enables embedding...
Graph Neural Network (GNN) methods Graph Convolutional Neural Networks (GCN)[4] Graph Attention Networks (GAT)[5] Hyperbolic Graph Convolutions (HGCN)[1] All models can be trained for Link prediction (lp) Node classification (nc) 2. Setup ...
Graph Neural Network (GNN) methods Graph Convolutional Neural Networks (GCN)[4] Graph Attention Networks (GAT)[5] Hyperbolic Graph Convolutions (HGCN)[1] All models can be trained for Link prediction (lp) Node classification (nc) 2. Setup ...
Hyperbolic graph convolutional networkIn current relation extraction tasks, when the input sentence structure is complex, the performance of in-context learning methods based on large language model is still lower than that of traditional pre-train fine-tune models. For complex sentence structures, ...
and recurrent neural networks, defining a hyperbolic neural network that bridges the gap between hyperbolic space and deep learning. Chami et al.15extended Graph convolutional neural networks(GCNs) to hyperbolic geometry, deriving operations for neural networks in hyperbolic space, including feature transf...
Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (TOG), 38(5):1–12, 2019. 4, 5 [44] Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox. Posecnn: A convolutional neural network for 6d object pose estimation in clut...
Lu et al. [11] proposed a collaborative graph learning model, CGL, for temporal event prediction in healthcare. This model sought to improve upon existing algorithms based on convolutional neural networks (CNNs) and attention mechanisms. The researchers employed a hierarchical embedding method for ...
Tong Z, Liang Y, Sun C, Li X, Rosenblum D, Lim A (2020a) Digraph inception convolutional networks. Advances in neural information processing systems Tong Z, Liang Y, Sun C, Rosenblum DS, Lim A (2020b) Directed graph convolutional network. arXiv preprint arXiv:2004.13970 Trivedi R, Faraj...
[1]Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space. [2]Evolvegcn: Evolving graph convolutional networks for dynamic graphs. [3]Variational graph recurrent neural networks. [4]DySAT: Deep neural representation learning on dynamic graphs via self-attention net...