Graph convolutional networks (GCNs) have received widespread attention in the field of knowledge graph embedding (KGE) due to their powerful graph modeling and neighbor information aggregation capabilities. The GCNs-based embedding methods typically use GCNs to aggregate neighbor entities or relationship ...
Graph convolutional neural networks (GCNs) on the other hand excel at capturing higher order information by applying multiple levels of aggregation to local representations. In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. We ...
Graph Attention Networks (GAT)[5] Hyperbolic Graph Convolutions (HGCN)[1] All models can be trained for Link prediction (lp) Node classification (nc) 2. Setup 2.1 Installation with conda If you don't have conda installed, please install it following the instructionshere. ...
@inproceedings{sun2021hgcf, title={HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering}, author={Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Perez, Maksims Volkovs}, booktitle={Proceedings of the International World Wide Web Conference}, year={2021} } ...
Graph convolutional neural networks In Euclidean space, feature transform and neighborhood aggregation are the crucial operations of graph convolutional neural networks, which can be simplified into the following formulas: $$\begin{aligned} h_{i}^{l,E} = W^{l}x_{i}^{l-1,E}+b^l \end{ali...
Addressing such issues, this paper proposes a Graph Learning model with Label Attention (GLLA) for temporal event prediction in healthcare to address these challenges and improve diagnostic prediction performance. The research focuses on two main aspects. Firstly, it uses graph neural networks to cap...
We show that networks of coplanar waveguide resonators can create a class of materials that constitute lattices in an effective hyperbolic space with constant negative curvature. We present numerical simulations of hyperbolic analogues of the kagome lattice that show unusual densities of states in which...
7. Conclusion In this work, we propose a dynamic hyperbolic graph neural network (DHANet) for hand object reconstruction. Our method applies hyperbolic neural networks for the first time in this task. By leveraging hyperbolic space, we de- sign a dynamic hyperbol...
This section briefly reviews the related work on the GFL framework and graph neural networks in hyperbolic space. Preliminaries This section describes the hyperbolic space and the basic arithmetic in the Poincaré ball model. The rules of a simple hyperbolic graph convolution network are introduced as...
Graph Attention Networks (GAT)[5] Hyperbolic Graph Convolutions (HGCN)[1] All models can be trained for Link prediction (lp) Node classification (nc) 2. Setup 2.1 Installation with conda If you don't have conda installed, please install it following the instructionshere. ...