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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 for graphs with heterophily: A survey
Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …
Neural decoding of EEG signals with machine learning: a systematic review
Electroencephalography (EEG) is a non-invasive technique used to record the brain's
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
evoked and induced electrical activity from the scalp. Artificial intelligence, particularly …
Graph anomaly detection with graph neural networks: Current status and challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is
an important task in the analysis of complex systems. Graph anomalies are patterns in a …
an important task in the analysis of complex systems. Graph anomalies are patterns in a …
Generative and contrastive self-supervised learning for graph anomaly detection
Anomaly detection from graph data has drawn much attention due to its practical
significance in many critical applications including cybersecurity, finance, and social …
significance in many critical applications including cybersecurity, finance, and social …
Hyperbolic deep neural networks: A survey
Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …
deep representations in the hyperbolic space provide high fidelity embeddings with few …
Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space
Representation learning over temporal networks has drawn considerable attention in recent
years. Efforts are mainly focused on modeling structural dependencies and temporal …
years. Efforts are mainly focused on modeling structural dependencies and temporal …
HRCF: Enhancing collaborative filtering via hyperbolic geometric regularization
In large-scale recommender systems, the user-item networks are generally scale-free or
expand exponentially. For the representation of the user and item, the latent features (aka …
expand exponentially. For the representation of the user and item, the latent features (aka …
Contrastive and generative graph convolutional networks for graph-based semi-supervised learning
Abstract Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …
Hyperbolic graph neural networks: A review of methods and applications
Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …
and have received widespread attention due to their impressive representation ability. In …