Node Property Prediction
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Benchmarks
Most implemented
SSD: Single Shot MultiBox Detector
Graph Attention Networks
Semi-Supervised Classification with Graph Convolutional Networks
Modeling Relational Data with Graph Convolutional Networks
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Inductive Representation Learning on Large Graphs
Papers
Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustwort…
Node Property PredictionGraph ClassificationLink PredictionA Fair Evaluation of Graph Foundation Models for Node Property Prediction
Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention. While many different types of models …
Node Property PredictionRecommendation SystemsGraph Neural NetworkFraud DetectiongHAWK: Local and Global Structure Encoding for Scalable Training of Graph Neural Networks on Knowledge Graphs
Knowledge Graphs (KGs) are a rich source of structured, heterogeneous data, powering a wide range of applications. A common approach to leverage this data is to train a graph neural network (GNN) on the KG. However, exis…
Node Property PredictionGraph Neural NetworkKnowledge GraphsLink PredictionEquivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph fou…
AllNode ClassificationNode Property PredictionProperty PredictionMixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
Graph neural networks excel at graph representation learning but struggle with heterophilous data and long-range dependencies. And graph transformers address these issues through self-attention, yet face scalability and …
Computational EfficiencyGraph Representation LearningMixture-of-Experts+3Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported o…
Node ClassificationNode Property Prediction