Feb 6, 2025 · We present a novel approach for edge weight prediction with guaranteed coverage. Focusing on the transductive setting, we define a series of GNN approaches to predict the …

Running the experiment requires Python3 with corresponding packages. The project is supported on Linux and MacOS. It may be possible to install on Windows, though this hasn't been …

Jun 13, 2024 · sportation systems to social networks. This paper describes a Graph Neural Network (GNN) approach for edge weigh. prediction with guaranteed cover-age. We leverage …

For sequential (i.e. linearly processed data representing graph growth) and nonsequential based temporal graphical data, predictive autoencoder architectures outperformed the classical …

Nov 18, 2016 · For the second step of the three-step procedure, we propose a new method, which optimizes edge weights through a local linear reconstruction error minimization under a …

Jan 7, 2021 · In this paper, we analyse the effect of reweighting edges of reconstruction losses when learning node embedding vectors for nodes of a graph with graph auto-encoders. The …

We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction …

Jun 3, 2020 · I am using a graph autoencoder to perform link prediction on a graph. The issue is that the number of negative (absent) edges is about 100 times the number of positive …

Jan 5, 2021 · In this paper, we propose a new model (in the form of an auto-encoder) to learn edge embeddings in (un)directed graphs. The encoder corresponds to a graph neural network …

In this paper, we analyse the effect of reweighting edges of reconstruction losses when learning node embedding vectors for nodes of a graph with graph auto-encoders. The analysis regards …