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Graph Reconstruction

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코드

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

https://git.skewed.de/count0/graph-tool

공식 코드

Disentangling homophily, community structure and triadic closure in networks

https://github.com/BlueGhostYi/BIGCF

공식 코드

Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering

https://github.com/G-AILab/IGRM

공식 코드

Data Imputation with Iterative Graph Reconstruction

https://github.com/JinheonBaek/GMT

공식 코드

Accurate Learning of Graph Representations with Graph Multiset Pooling

https://github.com/LJeub/Local2Global_embedding

공식 코드

Local2Global: Scaling global representation learning on graphs via local training

https://github.com/RingBDStack/AGE

공식 코드

A Robust and Generalized Framework for Adversarial Graph Embedding

https://github.com/amrhssn/teneNCE

공식 코드

Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks

https://github.com/andyweizhao/graphs-normed-spaces

공식 코드

Normed Spaces for Graph Embedding

https://github.com/everm0re/erarag-official

공식 코드

EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

https://github.com/fedelopez77/sympa

공식 코드

Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach

https://github.com/google-health/records-research

공식 코드

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

https://github.com/harryjo97/EHGNN

공식 코드

Edge Representation Learning with Hypergraphs

https://github.com/houchengbin/DynWalks

공식 코드

DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding

https://github.com/houchengbin/GloDyNE

공식 코드

GloDyNE: Global Topology Preserving Dynamic Network Embedding

https://github.com/insait-institute/grain

공식 코드

GRAIN: Exact Graph Reconstruction from Gradients

https://github.com/iyempissy/graph-stealing-attacks-with-explanation

공식 코드

Private Graph Extraction via Feature Explanations

https://github.com/jrwnter/pigvae

공식 코드

Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning

https://github.com/justsmart/RecFormer

공식 코드

Information Recovery-Driven Deep Incomplete Multiview Clustering Network

https://github.com/justsmart/Recformer-mindspore

공식 코드

Information Recovery-Driven Deep Incomplete Multiview Clustering Network

https://github.com/kali-hac/transg

공식 코드

TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification

https://github.com/kausable/causaldynamics

공식 코드

CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models

https://github.com/leotrs/glee

공식 코드

GLEE: Geometric Laplacian Eigenmap Embedding

https://github.com/lumia-group/graphparsingnetworks

공식 코드

Graph Parsing Networks

https://github.com/mtang724/nwr-gae

공식 코드

Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

https://github.com/niuchh/smp

공식 코드

Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing

https://github.com/palash1992/DynamicGEM

공식 코드

DynamicGEM: A Library for Dynamic Graph Embedding Methods

https://github.com/robinlu1209/st-gfsl

공식 코드

Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer

https://github.com/scsmuhio/MTGCN_Resources

공식 코드

Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language

https://github.com/snu-mllab/targeted-cause-discovery

공식 코드

Large-Scale Targeted Cause Discovery with Data-Driven Learning

https://github.com/thu-coai/JointGT

공식 코드

JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs

https://github.com/tmlr-group/mc-gra

공식 코드

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

https://github.com/woodfrog/heat

공식 코드

HEAT: Holistic Edge Attention Transformer for Structured Reconstruction

https://github.com/xiongbo010/qgcn

공식 코드

Pseudo-Riemannian Graph Convolutional Networks

https://github.com/xuewenyuan/TGRNet

공식 코드

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

https://github.com/yimutianyang/sigir23-vgcl

공식 코드

Generative Contrastive Graph Learning for Recommendation

https://github.com/yueming-yin/gafse

공식 코드

An open unified deep graph learning framework for discovering drug leads

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