{MAGIC}: Detecting advanced persistent threats via masked graph representation learning
Advance Persistent Threats (APTs), adopted by most delicate attackers, are becoming
increasing common and pose great threat to various enterprises and institutions. Data …
increasing common and pose great threat to various enterprises and institutions. Data …
[HTML][HTML] A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
State of the art and potentialities of graph-level learning
Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …
[PDF][PDF] Gapformer: Graph Transformer with Graph Pooling for Node Classification.
Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks.
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …
Graph neural networks for text classification: A survey
Text Classification is the most essential and fundamental problem in Natural Language
Processing. While numerous recent text classification models applied the sequential deep …
Processing. While numerous recent text classification models applied the sequential deep …
A review of graph-powered data quality applications for IoT monitoring sensor networks
The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
Comprehensive graph gradual pruning for sparse training in graph neural networks
Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …
exponentially increasing scale of graph data and a large number of model parameters …
Clustering for protein representation learning
Protein representation learning is a challenging task that aims to capture the structure and
function of proteins from their amino acid sequences. Previous methods largely ignored the …
function of proteins from their amino acid sequences. Previous methods largely ignored the …
Deep graph level anomaly detection with contrastive learning
Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern
and feature information are different from most normal graphs in a graph set, which is rarely …
and feature information are different from most normal graphs in a graph set, which is rarely …
Compressed heterogeneous graph for abstractive multi-document summarization
Multi-document summarization (MDS) aims to generate a summary for a number of related
documents. We propose HGSum—an MDS model that extends an encoder-decoder …
documents. We propose HGSum—an MDS model that extends an encoder-decoder …