Papers with Code 아카이브

Graph Representation Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 1
보존된 평가 기록: 1

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/weleen/AGRL.pytorch

공식 코드

Adaptive Graph Representation Learning for Video Person Re-identification

https://github.com/wendongbi/MMGNN

공식 코드

MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution

https://github.com/wenqifan03/gtn-sigir2022

공식 코드

Graph Trend Filtering Networks for Recommendations

https://github.com/wentao-xu/kge-cl

공식 코드

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

https://github.com/wjk666/disenkgat

공식 코드

DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network

https://github.com/wonderlandxd/wikg

공식 코드

Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis

https://github.com/woodcutter1998/gdgnn

공식 코드

Geodesic Graph Neural Network for Efficient Graph Representation Learning

https://github.com/workrep20232/proxi

공식 코드

PROXI: Challenging the GNNs for Link Prediction

https://github.com/wusw14/gnn-in-rs

공식 코드

Graph Neural Networks in Recommender Systems: A Survey

https://github.com/wuyucheng2002/ctaug

공식 코드

Graph Contrastive Learning with Cohesive Subgraph Awareness

https://github.com/wyy-code/TFP

공식 코드

Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding

https://github.com/wzh125/abaw-5

공식 코드

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

https://github.com/xiaojuantang/rule

공식 코드

RulE: Knowledge Graph Reasoning with Rule Embedding

https://github.com/xiaolongo/MaxMIAcrossFT

공식 코드

Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

https://github.com/xiaoxinhe/tape_arxiv_2023

공식 코드

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

https://github.com/xincanfeng/dimension

공식 코드

Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space

https://github.com/xincanfeng/ms_kge

공식 코드

Model-based Subsampling for Knowledge Graph Completion

https://github.com/xincanfeng/ss_kge

공식 코드

Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding

https://github.com/xjtuwgt/gnn-magna

공식 코드

Multi-hop Attention Graph Neural Network

https://github.com/xuehansheng/RepBin

공식 코드

RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning

https://github.com/xuehansheng/neurhap

공식 코드

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

https://github.com/xwhan/DeepPath

공식 코드

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

https://github.com/xxrep/golde

공식 코드

Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization

https://github.com/yandex-research/heterophilous-graphs

공식 코드

A critical look at the evaluation of GNNs under heterophily: Are we really making progress?

https://github.com/yaoyao-yaoyao-cell/mssm-gnn

공식 코드

Molecular Graph Representation Learning via Structural Similarity Information

https://github.com/yeeeqichen/ftm

공식 코드

FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning

https://github.com/yh-han/gsc

공식 코드

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

https://github.com/yh-utmsb/vepm

공식 코드

A Variational Edge Partition Model for Supervised Graph Representation Learning

https://github.com/yide-qiu/unikg

공식 코드

UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs

https://github.com/yifan-h/pge

공식 코드

A Representation Learning Framework for Property Graphs

https://github.com/yifeiacc/COSTA

공식 코드

COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning

https://github.com/yifeiwang15/motifconv

공식 코드

Motif-based Graph Representation Learning with Application to Chemical Molecules

https://github.com/yihongma/cilg-papers

공식 코드

Class-Imbalanced Learning on Graphs: A Survey

https://github.com/yihongt/dastnet

공식 코드

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

https://github.com/yihuazhu111/3h-th

공식 코드

3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding

https://github.com/yihuazhu111/orthogonale

공식 코드

Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding

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