#graph-neural-networks

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The HackerNoon Newsletter: Zuzalu is Dead. Long Live Zuzalu! - Metamorphosis Commenced v3 (11/6/2024) | HackerNoon

The article discusses significant tech news, emphasizing China's digital yuan's implications for privacy and the U.S. investigation into Tether's stablecoin operations.

Predicting Links in Graphs with Graph Neural Networks and DGL.ai | HackerNoon

Graphs represent complex relationships and tasks like link prediction are essential for practical applications.

Extending GNN Learning: 11 Additional Framework Applications | HackerNoon

The framework proposed has broad applications in GNNs, emphasizing fairness and k-shot learning under constraints of limited labeled data.
#topology-awareness

Understanding the Generalization Performance of GNNs: Topology Awareness and Future Directions | HackerNoon

GNNs' topology awareness is crucial for their generalization performance, particularly in semi-supervised tasks.

Exploring Topology Awareness, Generalization, and Active Learning in Graph Neural Networks | HackerNoon

Increasing topology awareness in GNNs doesn't guarantee improved generalization performance due to complexities in varying structures across domains.

Framework for Analyzing Topology Awareness and Generalization in Graph Neural Networks | HackerNoon

The framework investigates how topology awareness affects generalization performance in GNNs using metric distortion.

On the Topology Awareness and Generalization Performance of Graph Neural Networks: Main Results | HackerNoon

The study explores the relationship between generalization performance and structural distance in Graph Neural Networks, emphasizing the role of topology awareness.

Understanding Topology Awareness in Graph Neural Networks | HackerNoon

GNN topology awareness impacts generalization performance, revealing potential issues with unfair generalization across different structural groups.

Understanding the Generalization Performance of GNNs: Topology Awareness and Future Directions | HackerNoon

GNNs' topology awareness is crucial for their generalization performance, particularly in semi-supervised tasks.

Exploring Topology Awareness, Generalization, and Active Learning in Graph Neural Networks | HackerNoon

Increasing topology awareness in GNNs doesn't guarantee improved generalization performance due to complexities in varying structures across domains.

Framework for Analyzing Topology Awareness and Generalization in Graph Neural Networks | HackerNoon

The framework investigates how topology awareness affects generalization performance in GNNs using metric distortion.

On the Topology Awareness and Generalization Performance of Graph Neural Networks: Main Results | HackerNoon

The study explores the relationship between generalization performance and structural distance in Graph Neural Networks, emphasizing the role of topology awareness.

Understanding Topology Awareness in Graph Neural Networks | HackerNoon

GNN topology awareness impacts generalization performance, revealing potential issues with unfair generalization across different structural groups.
moretopology-awareness

Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness | HackerNoon

Our framework effectively analyzes GNN performance through a case study focused on shortest path distance, offering insights into real-world applications and graph learning problems.
#machine-learning

Graph Learning at the Scale of Modern Data Warehouses

Graph neural networks (GNNs) are advantageous for machine learning on graph data.
Deep learning has revolutionized complex tasks like computer vision and natural language processing.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

ADMET-AI is a machine learning platform predicting drug properties like absorption, distribution, metabolism, excretion, and toxicity.
Chemprop-RDKit uses a hybrid architecture to predict molecular properties, combining graph neural networks with feed-forward neural network layers.

Graph Learning at the Scale of Modern Data Warehouses

Graph neural networks (GNNs) are advantageous for machine learning on graph data.
Deep learning has revolutionized complex tasks like computer vision and natural language processing.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

ADMET-AI is a machine learning platform predicting drug properties like absorption, distribution, metabolism, excretion, and toxicity.
Chemprop-RDKit uses a hybrid architecture to predict molecular properties, combining graph neural networks with feed-forward neural network layers.
moremachine-learning
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