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Graph Representation Learning

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The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zjunet/damf

공식 코드

Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds

https://github.com/zjunet/dropmessage

공식 코드

DropMessage: Unifying Random Dropping for Graph Neural Networks

https://github.com/zjunlp/MKG_Analogy

공식 코드

Multimodal Analogical Reasoning over Knowledge Graphs

https://github.com/zjunlp/knn-kg

공식 코드

Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings

https://github.com/zjunlp/promptkg

공식 코드

LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings

https://github.com/zjunlp/relphormer

공식 코드

Relphormer: Relational Graph Transformer for Knowledge Graph Representations

https://github.com/zknus/iclr2024-frond

공식 코드

Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

https://github.com/zknus/torchfde

공식 코드

Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

https://github.com/zpengmei/SubFormer-Spec

공식 코드

Transformers are efficient hierarchical chemical graph learners

https://github.com/zwt233/NAFS

공식 코드

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

https://github.com/zxlearningdeep/latgrl

공식 코드

When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation Learning

https://github.com/zyang-16/MCNS

공식 코드

Understanding Negative Sampling in Graph Representation Learning

https://github.com/zyzisastudyreallyhardguy/graph-group-discrimination

공식 코드

Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination

https://github.com/zyzisastudyreallyhardguy/ugcl

공식 코드

Unifying Graph Contrastive Learning with Flexible Contextual Scopes

https://github.com/zzoomd/fairsad

공식 코드

Fair Graph Representation Learning via Sensitive Attribute Disentanglement

https://github.com/zzy979/gnn-recommendation

공식 코드

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

https://worksheets.codalab.org/worksheets/0xf215deb05edf44a2ac353c711f52a25f

공식 코드

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

https://github.com/2023-MindSpore-1/ms-code-20

A Generalization of ViT/MLP-Mixer to Graphs

https://github.com/AR13ar/Graph_Structure_Learning_for_Alzheimers

Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

https://github.com/AaltoPML/Rethinking-pooling-in-GNNs

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/Alishaba/pykeen-snapshot-ensembles

Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings

https://github.com/Aminsheykh98/INCREASE-pytorch

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

https://github.com/AnacletoLAB/ensmallen

GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding

https://github.com/AutoML-4Paradigm/AutoSF

Bilinear Scoring Function Search for Knowledge Graph Learning

https://github.com/AutoML-4Paradigm/AutoSF/tree/AutoSF-OGB

Bilinear Scoring Function Search for Knowledge Graph Learning

https://github.com/AutoML-4Paradigm/S2S

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/AutoML-Research/S2S

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/BorgwardtLab/SAT

Structure-Aware Transformer for Graph Representation Learning

https://github.com/CHFsky/GCC-paddle

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

https://github.com/CMSC470-Team/Model

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

https://github.com/CurryTang/Graph-LLM

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

https://github.com/DeepGraphLearning/graphvite

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

https://github.com/DeepGraphLearning/pLogicNet

Probabilistic Logic Neural Networks for Reasoning

https://github.com/Distance-awareNS/DNS

Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach

https://github.com/FDUDSDE/RDGSL

RDGSL: Dynamic Graph Representation Learning with Structure Learning

https://github.com/FDUDSDE/Subg-Con

Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.