Add to ORKGFactorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs). However, unlike GNNs, FMs struggle to incorporate node features and generalise to unseen nodes in inductive settings. Our work bridges the gap between FMs and GNNs by proposing ReFactor GNNs. This new architecture draws upon both modelling paradigms, which previously were largely thought of as disjoint. Concretely, using a message-passing formalism, we show how FMs can be cast as GNNs by reformulating the gradient descent procedure as message-passing operations, which forms the basis of our ReFactor GNNs. Across a multitude of well-established KGC benchmarks, our ReFa...
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-is...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and...
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn ...
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Predictive coding is a message-passing framework initially developed to model information processing...
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent ...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn...
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph L...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential per...
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-is...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and...
In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn ...
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Predictive coding is a message-passing framework initially developed to model information processing...
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent ...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn...
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph L...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
For node classification, Graph Neural Networks (GNN) assign predefined labels to graph nodes accordi...
Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which limits their potential per...
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-is...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and...