Papers Node Regression
“Node Regression” 태그가 달린 논문 13편 · 필터 해제
Inductive Graph Representation Learning with Quantum Graph Neural Networks
Quantum Graph Neural Networks (QGNNs) present a promising approach for combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are renowned for their scalability an…
Graph Representation LearningNode RegressionRepresentation LearningFully Distributed Online Training of Graph Neural Networks in Networked Systems
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Curren…
Node RegressionSchedulingNode Regression on Latent Position Random Graphs via Local Averaging
Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators for this task, we perform a theoretical …
Graph RegressionNode RegressionPositionregressionFIT-GNN: Faster Inference Time for GNNs Using Coarsening
Scalability of Graph Neural Networks (GNNs) remains a significant challenge, particularly when dealing with large-scale graphs. To tackle this, coarsening-based methods are used to reduce the graph into a smaller graph, …
ClassificationGraph ClassificationGraph RegressionNode Classification+2ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the …
Multivariate Time Series ForecastingNode RegressionTime SeriesTime Series ForecastingUplift Modeling Under Limited Supervision
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual interve…
Graph Neural NetworkNode RegressionInfinite Width Graph Neural Networks for Node Regression/ Classification
This work analyzes Graph Neural Networks, a generalization of Fully-Connected Deep Neural Nets on Graph structured data, when their width, that is the number of nodes in each fullyconnected layer is increasing to infinit…
Gaussian ProcessesGraph AttentionGraph LearningGraph Neural Network+3Analysis of different temporal graph neural network configurations on dynamic graphs
In recent years, there has been an increasing interest in the use of graph neural networks (GNNs) for analyzing dynamic graphs, which are graphs that evolve over time. However, there is still a lack of understanding of h…
Graph Neural NetworkNode RegressionTraffic PredictionGraph Neural Tangent Kernel: Convergence on Large Graphs
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can be hard to train on large-graph data, where their learning dynamics are not well understood. We investigate the training…
Node ClassificationNode RegressionregressionExplaining Dynamic Graph Neural Networks via Relevance Back-propagation
Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users from understanding and trusting the models,…
Link PredictionNode RegressionGNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework
Rule-based fine-grained IP geolocation methods are hard to generalize in computer networks which do not follow hypothetical rules. Recently, deep learning methods, like multi-layer perceptron (MLP), are tried to increase…
DecoderGraph Neural NetworkNode RegressionPropagation on Multi-relational Graphs for Node Regression
Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple gr…
Node RegressionregressionRelational ReasoningTransfer Learning for Node Regression Applied to Spreading Prediction
Understanding how information propagates in real-life complex networks yields a better understanding of dynamic processes such as misinformation or epidemic spreading. The recently introduced branch of machine learning m…
MisinformationNode RegressionregressionTransfer Learning