The awesome-few-shot-gnn from marnymk

Graph Meta-Learning/Graph Few-Shot Learning

The repository contains links primarily to conference and journal publications about graph meta-learning and graph few/zero-shot learning. You are encouraged to contribute to this repo!

Node-level Task

Name	Paper	Code
Meta-GNN	[CIKM 2019] Meta-GNN: On Few-shot Node Classification in Graph Meta-learning	PyTorch
GPN	[CIKM 2020] Graph Prototypical Networks for Few-shot Learning on Attributed Networks	PyTorch
AMM-GNN	[CIKM 2020] Graph Few-shot Learning with Attribute Matching	[N/A]
G-Meta	[NeurIPS 2020] Graph Meta Learning via Local Subgraphs	PyTorch
MetaTNE	[NuerIPS 2020] Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding	PyTorch
M3S	[AAAI 2020] Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes	TensorFlow
RALE	[AAAI 2021] Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph	TensorFlow
Meta-GDN	[WWW 2021] Few-shot Network Anomaly Detection via Cross-network Meta-learning	PyTorch
Mixup	[WWW 2021] Mixup for Node and Graph Classification	[N/A]
HAG-Meta	[WSDM 2022] Graph few-shot class-incremental learning	PyTorch
AutoGRL	[IJCNN 2022] Automated Graph Representation Learning for Node Classification	PyTorch
MuL-GRN	[TKDE 2022] MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification	[N/A]
Meta-GPS	[SIGIR 2022] Few-shot Node Classification on Attributed Networks with Graph Meta-learning	[N/A]
TENT	[SIGKDD 2022] Task-Adaptive Few-shot Node Classification	PyTorch
KnowPrompt	[WWW 2022] KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction	PyTorch
IA-FSNC	[IJCAI 2022] Information Augmentation for Few-shot Node Classifcation	[N/A]
SGCL	[ECML 2022] Supervised Graph Contrastive Learning for Few-shot Node Classification	[N/A]
LADSL	[Frontiers 2022] Few-shot node classification via local adaptive discriminant structure learning	[N/A]
Meta-GIN	[arXiv 2022] Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification	[N/A]
LGLNN	[NN 2022] LGLNN: Label Guided Graph Learning-Neural Network for few-shot learning	[N/A]
TLP	[LOG 2022] Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification	PyTorch

Edge-level Task

Name	Paper	Code
GMatching	[EMNLP 2018] One-Shot Relational Learning for Knowledge Graphs	PyTorch
FSRL	[AAAI 2019] Few-Shot Knowledge Graph Completion	PyTorch
MetaR	[EMNLP 2019] Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs	PyTorch
FAAN	[EMNLP 2020] Adaptive Attentional Network for Few-Shot Knowledge Graph Completion	PyTorch
ZSGAN	[AAAI 2020] Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs	PyTorch
GEN	[NeurIPS 2020] Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction	PyTorch
CSR	[NeurIPS 2022] Few-shot Relational Reasoning via Connection Subgraph Pretraining	PyTorch

Graph-level Task

Name	Paper	Code
GFL	[AAAI 2020] Graph Few-Shot Learning via Knowledge Transfer	[N/A]
G-META	[NuerIPS 2020] Graph Meta Learning via Local Subgraphs	PyTorch
AS-MAML	[CIKM 2020] Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification	PyTorch
Super-class	[ICLR 2020] Few-shot learning on graphs via super-classes based on graph spectral measures	PyTorch
Meta-MGNN	[WWW2021] Few-Shot Graph Learning for Molecular Property Prediction	PyTorch
MI-GNN	[SIGIR 2021] Meta-Inductive Node Classification across Graphs	PyTorch
CuCo	[IJCAI 2021] CuCo: Graph Representation with Curriculum Contrastive Learning	PyTorch
GFL-RTG	[TNNLS 2022] Graph Few-Shot Learning via Restructuring Task Graph	[N/A]
FAITH	[IJCAI 2022] FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs	PyTorch

Acknowledgement

This page is contributed and maintained by Kaize Ding ([email protected]), Zhen Tan ([email protected]), and Song Wang ([email protected]).

marnymk / awesome-few-shot-gnn Goto Github PK

awesome-few-shot-gnn's Introduction

Graph Meta-Learning/Graph Few-Shot Learning

Node-level Task

Edge-level Task

Graph-level Task

Acknowledgement

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