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GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Preserved Fulltext
In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. ...
The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and ...
MR- GNN [Xu et al., 2019] is an end-to-end graph neural network with multi-resolution architecture that produces interaction between pairs of chemical graphs. ...
doi:10.24963/ijcai.2020/183
dblp:conf/ijcai/WangL0Q020
fatcat:gj6tkazrfbceflyuyxtbmn4kxq
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. ...
The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and ...
MR- GNN [Xu et al., 2019] is an end-to-end graph neural network with multi-resolution architecture that produces interaction between pairs of chemical graphs. ...
arXiv:2005.05537v1
fatcat:ipqoahg5wzcohbj3k7dedsygbm
A multi-scale feature fusion model based on biological knowledge graph and transformer-encoder for drug-drug interaction prediction
[article]
2024
bioRxiv
pre-print
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The file type is application/pdf.
Finally, we employ a transformer encoder to fuse the multi-scale drug representations and feed the resulting drug pair vector into a fully connected neural network for prediction. ...
Next, ALG-DDI leverages heterogeneous graphs to capture the local biological information between drugs and several highly related biological entities. ...
for a deep neural network to make predictions. ...
doi:10.1101/2024.01.12.575305
fatcat:i7etz4fqzjcidknnap26tw3wbu
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs
[article]
2023
arXiv
pre-print
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Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large networks, including drug-drug ...
them to the decoding stage for link prediction. ...
Related Work
Link Prediction with Graph Neural Networks Graph neural networks (GNNs) are deep learning models that learn to generate representations on graph-structured data. ...
arXiv:2302.08680v1
fatcat:rd52ih4pv5fijn5peuiwjfq5ju
Graph Neural Networks: A Review of Methods and Applications
[article]
2021
arXiv
pre-print
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Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. ...
In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important ...
MR-GNN (Xu et al., 2019d) introduces a multi-resolution approach to extract and summarize local and global features for better prediction. Biomedical Engineering. ...
arXiv:1812.08434v6
fatcat:ncz44kny6nairjjnysrqd5qjoi
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
[article]
2021
arXiv
pre-print
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Other Versions
graph neural network. ...
We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between structural features. ...
.: Mr-gnn: Multi-resolution and dual graph neural network for predicting structured entity interactions. ...
arXiv:2106.13061v4
fatcat:cuplpgdgqvg3dhy5txggcux73q