Node Property Prediction
50 papers with code • 5 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Node Property Prediction
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Libraries
Use these libraries to find Node Property Prediction models and implementations
OGB
Most implemented papers
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
PaddlePaddle/PGL
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Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.
Robust Optimization as Data Augmentation for Large-scale Graphs
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
XiaoxinHe/TAPE
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With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs.
Long-range Meta-path Search on Large-scale Heterogeneous Graphs
jhl-hust/ldmlp
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To this end, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS).
Bag of Tricks for Node Classification with Graph Neural Networks
espylapiza/Bag-of-Tricks-for-Node-Classification-with-Graph-Neural-Networks
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Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.
Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
vita-group/large_scale_gcn_benchmarking
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Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
ivam-he/pshgcn
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Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness.
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
shenyanghuang/tgb
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NeurIPS 2023
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
