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Found 10 papers, 7 papers with code
Explainable Graph Neural Networks for Observation Impact Analysis in Atmospheric State Estimation
This paper investigates the impact of observations on atmospheric state estimation in weather forecasting systems using graph neural networks (GNNs) and explainability methods.
CloudNine: Analyzing Meteorological Observation Impact on Weather Prediction Using Explainable Graph Neural Networks
Combining an XGNN-based atmospheric state estimation model with a numerical weather prediction model, we provide a web application to search for observations in the 3D space of the Earth system and to visualize the impact of individual observations on predictions in specific spatial regions and time periods.
A Survey on Structure-Preserving Graph Transformers
The transformer architecture has shown remarkable success in various domains, such as natural language processing and computer vision.
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
In this paper, we propose Community-aware Graph Transformers, namely CGT, to learn degree-unbiased representations based on learnable augmentations and graph transformers by extracting within community structures.
Ranked #1 on
Node Clustering
on Pubmed
(Conductance metric)
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG.
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations.
Ranked #1 on
Node Clustering
on Actor
Connector 0.5: A unified framework for graph representation learning
Second, we introduce various graph representation learning models, ranging from shallow to deep graph embedding models.
Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network
By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance.
Ranked #1 on
Solar Irradiance Forecasting
on ASOS Data
Learning multi-resolution representations of research patterns in bibliographic networks
This study aims at representing research patterns of bibliographic entities (e. g., scholars, papers, and venues) with a fixed-length vector.
Ranked #1 on
Research Performance Prediction
on AMiner
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?
Based on embedding the collaboration patterns, we have clustered scholars according to their collaboration styles.
