The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs). R-GCNs generate representations for nodes of interest by repeatedly aggregating parameterised, relation-specific transformations of their neighbours. However, in this paper, we argue that the the R-GCN's main contribution lies in this "message passing" paradigm, rather than the learned weights. To this end, we introduce the "Random Relational Graph Convolutional Network" (RR-GCN), which leaves all parameters untrained and thus constructs node embeddings by aggregating randomly transformed random representations fr...
Collective inference is widely used to improve classification in network datasets. However, despite ...
Recently, several variants of graph convolution networks (GCNs), which have shown awesome performanc...
In the last decade, connectionist models have been proposed that can process structured information ...
In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Us...
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Usin...
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling to...
Graph neural networks (GNNs) are effective models for representation learning on relational data. Ho...
Knowledge graphs enable a wide variety of applications, including question answering and information...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Knowledge Graphs contain factual information about the world, and providing a structural representa...
Recent years have witnessed a rise in real-world data captured with rich structural information that...
Relational data representations have become an increasingly important topic due to the recent prolif...
In the last decade, connectionist models have been proposed that can process structured information ...
International audienceRelational Graph Convolutional Networks (RGCNs) identify relationships within ...
Collective inference is widely used to improve classification in network datasets. However, despite ...
Collective inference is widely used to improve classification in network datasets. However, despite ...
Recently, several variants of graph convolution networks (GCNs), which have shown awesome performanc...
In the last decade, connectionist models have been proposed that can process structured information ...
In this article, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Us...
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Usin...
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling to...
Graph neural networks (GNNs) are effective models for representation learning on relational data. Ho...
Knowledge graphs enable a wide variety of applications, including question answering and information...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Knowledge Graphs contain factual information about the world, and providing a structural representa...
Recent years have witnessed a rise in real-world data captured with rich structural information that...
Relational data representations have become an increasingly important topic due to the recent prolif...
In the last decade, connectionist models have been proposed that can process structured information ...
International audienceRelational Graph Convolutional Networks (RGCNs) identify relationships within ...
Collective inference is widely used to improve classification in network datasets. However, despite ...
Collective inference is widely used to improve classification in network datasets. However, despite ...
Recently, several variants of graph convolution networks (GCNs), which have shown awesome performanc...
In the last decade, connectionist models have been proposed that can process structured information ...