Magnet: A neural network for directed graphs
The prevalence of graph-based data has spurred the rapid development of graph neural
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …
networks (GNNs) and related machine learning algorithms. Yet, despite the many datasets …
Directed graph contrastive learning
Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
Edge directionality improves learning on heterophilic graphs
Abstract Graph Neural Networks (GNNs) have become the de-facto standard tool for
modeling relational data. However, while many real-world graphs are directed, the majority …
modeling relational data. However, while many real-world graphs are directed, the majority …
Breaking the entanglement of homophily and heterophily in semi-supervised node classification
Recently, graph neural networks (GNNs) have shown prominent performance in semi-
supervised node classification by leveraging knowledge from the graph database. However …
supervised node classification by leveraging knowledge from the graph database. However …
A fractional graph laplacian approach to oversmoothing
Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …
applications. However, GNNs often struggle to capture long-range dependencies in graphs …
Msgnn: A spectral graph neural network based on a novel magnetic signed laplacian
Signed and directed networks are ubiquitous in real-world applications. However, there has
been relatively little work proposing spectral graph neural networks (GNNs) for such …
been relatively little work proposing spectral graph neural networks (GNNs) for such …
Gnnrank: Learning global rankings from pairwise comparisons via directed graph neural networks
Recovering global rankings from pairwise comparisons has wide applications from time
synchronization to sports team ranking. Pairwise comparisons corresponding to matches in …
synchronization to sports team ranking. Pairwise comparisons corresponding to matches in …
Minimum entropy principle guided graph neural networks
Graph neural networks (GNNs) are now the mainstream method for mining graph-structured
data and learning low-dimensional node-and graph-level embeddings to serve downstream …
data and learning low-dimensional node-and graph-level embeddings to serve downstream …
ST-TDCN: A two-channel tree-structure spatial–temporal convolutional network model for traffic velocity prediction
In the development of intelligent transport systems which aims to provide safe, convenient,
and comfortable transport and effectively addresses issues with traffic congestion and …
and comfortable transport and effectively addresses issues with traffic congestion and …
Curriculum graph machine learning: A survey
Graph machine learning has been extensively studied in both academia and industry.
However, in the literature, most existing graph machine learning models are designed to …
However, in the literature, most existing graph machine learning models are designed to …