A substructure|aware graph neural network incorporating relation ...

A substructure‐aware graph neural network incorporating relation ...

Identifying drug–drug interactions (DDIs) is an important aspect of drug design research, and predicting DDIs serves as a crucial guarantee ...

Substructure Aware Graph Neural Networks

We propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues.

A substructure-aware graph neural network incorporating relation ...

A substructure-aware graph neural network incorporating relation features for drug-drug interaction prediction · 期刊 · 出版社 · 关键词 · 类别 · 资金 · 向作者/读者 ...

Substructure Aware Graph Neural Networks | Request PDF

... Subgraph representation learning not only converges the node and edge information but also fully incorporates the subgraph information [3]. The [4] . .

Structure-aware graph neural network based deep transfer learning ...

We also incorporate a 'Base' model, which always uses the average property value of all the training data provided to it as the predicted ...

Substructure Aware Graph Neural Networks

Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Sub-.

Structure and position-aware graph neural network for airway labeling

Therefore, an SPGNN layer without positional encodings is a graph attention network layer with a skip connection. ... incorporating positional encoding as ...

Sequence-Aware Graph Neural Network Incorporating ...

We propose sequence-aware graph neural network incorporating neighborhood information, named SAN-GNN. We construct a session graph and a neighborhood graph to ...

Relating-Up: Advancing Graph Neural Networks through Inter ... - arXiv

Relating-Up, a plug-and-play module that enhances GNNs by exploiting inter-graph relationships. This module incorporates a relation-aware encoder and a ...

Relation Structure-Aware Heterogeneous Graph Neural Network

exploring and integrating associations between different types of edges in heterogeneous graphs using graph neural network. In comparison, existing methods ...

Integrating Topic-Aware Heterogeneous Graph Neural Network With ...

In addition to topic modeling, the framework utilized a Heterogeneous Graph Neural Network (HGNN), capable of capturing the relationship ...

Chain-aware graph neural networks for molecular property prediction

... connection (IRDC). Then the molecular graph is represented by attentive pooling of all node representations. Compared to standard graph ...

Semantic- and Relation-Aware Heterogeneous Graph Neural Network

We propose a novel Semantic- and Relation-aware Heterogeneous Graph neural Network, dubbed SR-HGN, which jointly incorporates rich semantics preserved on nodes ...

Position-aware Graph Neural Networks

P-GNNs have several advantages: they are inductive, scalable, and can incorporate node feature information. We apply P-GNNs to multiple prediction tasks includ-.

Knowledge-aware Graph Neural Networks with Label Smoothness ...

function that identifies important knowledge graph relationships for a given ... [30] algorithms, then incorporate learned entity embeddings into.

Relation-aware Graph Convolutional Networks for Multi-relational ...

The advancement in graph neural network (GNN) ... graph to the primal graph, thus incorporating relation information to the entity representations.

Improving Graph Neural Network Expressivity via Subgraph...

... aware message passing scheme based on substructure encoding. We show that our architecture allows incorporating domain-specific inductive biases and that it ...

Chemistry-intuitive explanation of graph neural networks for ... - Nature

... incorporated into a RGCN under the relation r\epsilon R. The weight ... graph neural networks for molecular property prediction with substructure ...

Time-aware Graph Neural Network for Entity Alignment between ...

We embed entities, relations and timestamps of different KGs into a vector space and use GNNs to learn entity representations. To incorporate both relation ...

MRA-GNN: Minutiae Relation-Aware Model over Graph Neural ...

... Relation-Aware model over Graph Neural Network (MRA-GNN). Our proposed approach incorporates a GNN-based framework in fingerprint embedding ...