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 …
Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer
Histopathology, which is the gold-standard for prostate cancer diagnosis, faces significant
challenges. With prostate cancer ranking among the most common cancers in the United …
challenges. With prostate cancer ranking among the most common cancers in the United …
Hope: High-order graph ode for modeling interacting dynamics
Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
strategies to model interacting multi-agent dynamical systems in a data-driven approach …
Self-supervised graph-level representation learning with adversarial contrastive learning
The recently developed unsupervised graph representation learning approaches apply
contrastive learning into graph-structured data and achieve promising performance …
contrastive learning into graph-structured data and achieve promising performance …
Redundancy-free self-supervised relational learning for graph clustering
Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …
a fundamental yet challenging task in data analysis and has received considerable attention …
GTC: GNN-Transformer co-contrastive learning for self-supervised heterogeneous graph representation
Abstract Graph Neural Networks (GNNs) have emerged as the most powerful weapon for
various graph tasks due to the message-passing mechanism's great local information …
various graph tasks due to the message-passing mechanism's great local information …
[HTML][HTML] Self-supervised contrastive graph representation with node and graph augmentation
Graph representation is a critical technology in the field of knowledge engineering and
knowledge-based applications since most knowledge bases are represented in the graph …
knowledge-based applications since most knowledge bases are represented in the graph …
Learning graph ode for continuous-time sequential recommendation
Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …
behavior correlations, which are usually represented as the item purchasing sequences …
[HTML][HTML] Portable graph-based rumour detection against multi-modal heterophily
The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …
various techniques for graph-based rumour detection have been proposed recently. Existing …
3D graph neural network with few-shot learning for predicting drug–drug interactions in scaffold-based cold start scenario
Understanding drug–drug interactions (DDI) of new drugs is critical for minimizing
unexpected adverse drug reactions. The modeling of new drugs is called a cold start …
unexpected adverse drug reactions. The modeling of new drugs is called a cold start …