A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
A comprehensive survey on graph anomaly detection with deep learning
Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …
the others in the sample. Over the past few decades, research on anomaly mining has …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Explainable artificial intelligence applications in cyber security: State-of-the-art in research
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …
Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …
Pick and choose: a GNN-based imbalanced learning approach for fraud detection
Graph-based fraud detection approaches have escalated lots of attention recently due to the
abundant relational information of graph-structured data, which may be beneficial for the …
abundant relational information of graph-structured data, which may be beneficial for the …
Anomaly detection on attributed networks via contrastive self-supervised learning
Anomaly detection on attributed networks attracts considerable research interests due to
wide applications of attributed networks in modeling a wide range of complex systems …
wide applications of attributed networks in modeling a wide range of complex systems …
A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Heterogeneous graph neural network
Representation learning in heterogeneous graphs aims to pursue a meaningful vector
representation for each node so as to facilitate downstream applications such as link …
representation for each node so as to facilitate downstream applications such as link …
Intelligent financial fraud detection practices in post-pandemic era
The great losses caused by financial fraud have attracted continuous attention from
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …