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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/ringbdstack/icl-incremental-infonce

공식 코드

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

https://github.com/ringbdstack/sgdd

공식 코드

Does Graph Distillation See Like Vision Dataset Counterpart?

https://github.com/rinnesz/clga

공식 코드

Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation

https://github.com/rising0321/hmod

공식 코드

Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction

https://github.com/ritatsousa/expressionKGplus

공식 코드

Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs

https://github.com/rongrongma/cos-gnn

공식 코드

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

https://github.com/rongrongma/mosgnn

공식 코드

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

https://github.com/roshnigiyer/nmm

공식 코드

Non-Euclidean Mixture Model for Social Network Embedding

https://github.com/royull/fairedit

공식 코드

FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

https://github.com/rucaibox/dpgnn

공식 코드

Modeling Two-Way Selection Preference for Person-Job Fit

https://github.com/ruiz-lab/shortest-path

공식 코드

Local Distance-Preserving Node Embeddings and Their Performance on Random Graphs

https://github.com/rusty1s/pytorch_geometric

공식 코드

Fast Graph Representation Learning with PyTorch Geometric

https://github.com/rutgerswiselab/NS-KGE

공식 코드

Efficient Non-Sampling Knowledge Graph Embedding

https://github.com/rxphd/lazy_gnn

공식 코드

LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

https://github.com/ryienh/dggen

공식 코드

A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation

https://github.com/safreita1/malnet-graph

공식 코드

A Large-Scale Database for Graph Representation Learning

https://github.com/samihaija/isvd

공식 코드

Implicit SVD for Graph Representation Learning

https://github.com/samihaija/tf-fsvd

공식 코드

Fast Graph Learning with Unique Optimal Solutions

https://github.com/samsungsds-research-papers/lgvr

공식 코드

Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning

https://github.com/sara-silvaad/connectome_gcl

공식 코드

Graph Contrastive Learning for Connectome Classification

https://github.com/sbonner0/temporal-offset-reconstruction

공식 코드

Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning

https://github.com/scd158/RMNA

공식 코드

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining

https://github.com/scwu1008/kadm

공식 코드

Unify Local and Global Information for Top-$N$ Recommendation

https://github.com/seeyourmind/ptbox

공식 코드

Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding

https://github.com/sennadir/gcorn

공식 코드

Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks

https://github.com/seukgcode/fastkge

공식 코드

Fast and Continual Knowledge Graph Embedding via Incremental LoRA

https://github.com/seukgcode/incde

공식 코드

Towards Continual Knowledge Graph Embedding via Incremental Distillation

https://github.com/sezinata/SurveyDGP

공식 코드

Recent Advances in Network-based Methods for Disease Gene Prediction

https://github.com/sh-je/eans

공식 코드

Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph Embedding

https://github.com/shaoxiongji/awesome-knowledge-graph

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A Survey on Knowledge Graphs: Representation, Acquisition and Applications

https://github.com/sharecodesubmission/weighted_product_manifold

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Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold

https://github.com/shawnkx/Decom

공식 코드

Decompressing Knowledge Graph Representations for Link Prediction

https://github.com/shengyu-feng/ariel

공식 코드

Adversarial Graph Contrastive Learning with Information Regularization

https://github.com/shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks

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Deep Network Embedding for Graph Representation Learning in Signed Networks

https://github.com/shifengxie/sgec

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Variational Graph Contrastive Learning

https://github.com/shifengxie/subgec

공식 코드

Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning

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