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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/shweta-jacob/ssnp

공식 코드

Stochastic Subgraph Neighborhood Pooling for Subgraph Classification

https://github.com/simonepiaggesi/hosgns

공식 코드

Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling

https://github.com/simonzhang00/GraphExtendedFiltrationLearning

공식 코드

GEFL: Extended Filtration Learning for Graph Classification

https://github.com/skeletondyh/gred

공식 코드

Recurrent Distance Filtering for Graph Representation Learning

https://github.com/skojaku/residual2vec

공식 코드

Residual2Vec: Debiasing graph embedding with random graphs

https://github.com/skyorca/awesome-graph-domain-adaptation-papers

공식 코드

Graph Domain Adaptation: Challenges, Progress and Prospects

https://github.com/smufang/fewshotgraph

공식 코드

A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning

https://github.com/snap-stanford/distance-encoding

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Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning

https://github.com/snap-stanford/roland

공식 코드

ROLAND: Graph Learning Framework for Dynamic Graphs

https://github.com/snudatalab/AugWard

공식 코드

AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification

https://github.com/sofiperez91/lase

공식 코드

LASE: Learned Adjacency Spectral Embeddings

https://github.com/soledad921/ATISE

공식 코드

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

https://github.com/soledad921/ATISE/blob/master/README.md

공식 코드

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

https://github.com/sowonjeong/lobstur-graph-bootstrap

공식 코드

LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks

https://github.com/sserranw/abg-nas

공식 코드

ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning

https://github.com/ssgood/mmgl

공식 코드

Multi-modal Graph Learning for Disease Prediction

https://github.com/ssysteve/learning-graph-representation-with-task-specific-topology-and-multi-dimensional-edge-features

공식 코드

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

https://github.com/stanfordasl/rl4amod

공식 코드

Robo-taxi Fleet Coordination at Scale via Reinforcement Learning

https://github.com/stasl0217/beurre

공식 코드

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

https://github.com/stefanosantaris/DMTKG

공식 코드

Distill2Vec: Dynamic Graph Representation Learning with Knowledge Distillation

https://github.com/stefanosantaris/vstreamdrls

공식 코드

VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions

https://github.com/suchun-sv/sgdd

공식 코드

Does Graph Distillation See Like Vision Dataset Counterpart?

https://github.com/summerwings/gcn_anticipation

공식 코드

Towards Graph Representation Learning Based Surgical Workflow Anticipation

https://github.com/sunjss/cocn

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Scalable Graph Compressed Convolutions

https://github.com/superallen13/GCondenser

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GCondenser: Benchmarking Graph Condensation

https://github.com/superallen13/puma

공식 코드

PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation

https://github.com/sycny/engage

공식 코드

ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning

https://github.com/systemsecuritystorm/awesome-binary-similarity

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VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity

https://github.com/syvail/PairE-Graph-Representation-Learning-Beyond-Node-and-Homophily

공식 코드

Graph Representation Learning Beyond Node and Homophily

https://github.com/tata1661/shine-emnlp21

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Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification

https://github.com/tegusi/EAGRNet

공식 코드

Edge-aware Graph Representation Learning and Reasoning for Face Parsing

https://github.com/terrypei/nnretrieval

공식 코드

Neural Architecture Retrieval

https://github.com/tewiSong/Rot-Pro

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Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding

https://github.com/tgebhart/sheaf_kg

공식 코드

Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding

https://github.com/tgebhart/sheaf_kg_transind

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Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference

https://github.com/thilinicooray/deepgcfx

공식 코드

Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning

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