Substructure Aware Graph Neural Networks

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

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-.

SAGNN-Substructure-Aware-Graph-Neural-Networks - GitHub

Official implementation of SAGNN. Contribute to BlackHalo-Drake/SAGNN-Substructure-Aware-Graph-Neural-Networks development by creating an account on GitHub.

Explaining compound activity predictions with a substructure-aware ...

Explaining compound activity predictions with a substructure-aware loss for graph neural networks. Kenza Amara,; Raquel Rodríguez-Pérez ...

Substructure aware graph neural networks - ACM Digital Library

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper ...

Substructure Aware Graph Neural Networks - Semantic Scholar

This work proposes a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN), which theoretically proves that the ...

Explaining compound activity predictions with a substructure-aware ...

low performance for popular deep learning algorithms such as graph neural networks. (GNNs), especially when compared with simpler modeling alternatives such ...

Learning size-adaptive molecular substructures for explainable drug ...

In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism.

SSR-DTA: Substructure-aware multi-layer graph neural networks for ...

Highlights. •. We introduce a substructure-aware multi-layer graph network framework for the prediction of the drug-target binding affinity task ...

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 ...

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

Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture

SHGNN: A Substructure-Aware and High Expressive Graph Neural ...

Graph Neural Networks (GNNs), with their remarkable capabilities in learning graph features, have achieved success in graph classification ...

Substructure Aware Graph Neural Networks | Request PDF

... neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues. We first propose a Cut subgraph which can be ...

Counting Graph Substructures with Graph Neural Networks

Graph Neural Networks (GNNs) are powerful representation learning tools that have achieved remarkable performance in various downstream tasks.

Can They Count Substructures? - KDD 2024

[18] introduced the. Identity-Aware Graph Neural Networks (ID-GNNs), which use node identifiers to break symmetries in the graph, allowing for ...

Substructure Aware Graph Neural Networks - SlideShare

240729_Thuy_Labseminar[Substructure Aware Graph Neural Networks].pptx · 1 of 14 · 2 Problem: Graph isomorphism • Graph isomorphism is a ...

Explaining compound activity predictions with a substructure-aware ...

Explaining compound activity predictions with a substructure-aware loss for graph neural networks. J Cheminform. 2023 Jul 25;15(1):67. doi: 10.1186/s13321- ...

KSGTN-DDI: Key Substructure-aware Graph Transformer Network ...

Drug substructure plays a crucial role in predicting drug-drug interaction (DDI) with combination drugs for disease therapies.

Improving Graph Neural Network Expressivity via Subgraph ... - arXiv

We propose "Graph Substructure Networks" (GSN), a topologically-aware message passing scheme based on substructure encoding.

Activity · BlackHalo-Drake/SAGNN-Substructure-Aware ... - GitHub

Official implementation of SAGNN. Contribute to BlackHalo-Drake/SAGNN-Substructure-Aware-Graph-Neural-Networks development by creating an account on GitHub.