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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/box-el/boxel

공식 코드

Faithiful Embeddings for EL++ Knowledge Bases

https://github.com/br0kej/kyn

공식 코드

Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets

https://github.com/brianlsy98/dice

공식 코드

DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining

https://github.com/bupt-gamma/igm

공식 코드

Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization

https://github.com/bywmm/Bi-GCN

공식 코드

Bi-GCN: Binary Graph Convolutional Network

https://github.com/bzho3923/magnet

공식 코드

Robust Graph Representation Learning for Local Corruption Recovery

https://github.com/cai-lw/KBGAN

공식 코드

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

https://github.com/carolinlawrence/gradient-rollback

공식 코드

Explaining Neural Matrix Factorization with Gradient Rollback

https://github.com/causalkg/causallp

공식 코드

CausalLP: Learning causal relations with weighted knowledge graph link prediction

https://github.com/chandlerbang/gtrans

공식 코드

Empowering Graph Representation Learning with Test-Time Graph Transformation

https://github.com/changyi7231/nfe

공식 코드

Knowledge Graph Embedding by Normalizing Flows

https://github.com/chao1224/graphmvp

공식 코드

Pre-training Molecular Graph Representation with 3D Geometry

https://github.com/chenhaoxing/MACL_ICML2023

공식 코드

Model-Aware Contrastive Learning: Towards Escaping the Dilemmas

https://github.com/chenhongruixuan/srgcae

공식 코드

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

https://github.com/chenjie20/SSCDL

공식 코드

Conditional Distribution Learning on Graphs

https://github.com/chenjie20/cgcl

공식 코드

Cross-View Graph Consistency Learning for Invariant Graph Representations

https://github.com/chenpenggang/wlaligncode

공식 코드

WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning

https://github.com/chirag126/nifty

공식 코드

Towards a Unified Framework for Fair and Stable Graph Representation Learning

https://github.com/christiangerloff/ibn-asd-classification

공식 코드

Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?

https://github.com/christopher-irw/proformer_ce

공식 코드

Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring

https://github.com/chrystalii/cngcf

공식 코드

Neural Causal Graph Collaborative Filtering

https://github.com/claudmor/multi_task_multi_class_neural_directed_link_prediction

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Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction

https://github.com/cmhimself/quest

공식 코드

Querying functional and structural niches on spatial transcriptomics data

https://github.com/conf20/egg

공식 코드

Embedding Graphs on Grassmann Manifold

https://github.com/cptq/SignNet-BasisNet

공식 코드

Sign and Basis Invariant Networks for Spectral Graph Representation Learning

https://github.com/crisostomi/metric-few-shot-graph

공식 코드

Metric Based Few-Shot Graph Classification

https://github.com/cvi-yangwn/benchmark-gnn-for-multivariate-time-series-classification

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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

https://github.com/czczup/gptrans

공식 코드

Graph Propagation Transformer for Graph Representation Learning

https://github.com/daiquocnguyen/Graph-Transformer

공식 코드

Universal Graph Transformer Self-Attention Networks

https://github.com/daiquocnguyen/QGNN

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Quaternion Graph Neural Networks

https://github.com/daiquocnguyen/R-MeN

공식 코드

A Relational Memory-based Embedding Model for Triple Classification and Search Personalization

https://github.com/dair-iitd/kbi

공식 코드

Joint Matrix-Tensor Factorization for Knowledge Base Inference

https://github.com/dalab/hyperbolic_nn

공식 코드

Hyperbolic Neural Networks

https://github.com/danielegrandi-adsk/material-gnn

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Material Prediction for Design Automation Using Graph Representation Learning

https://github.com/data-engineering-and-semantics/openkgbiblio

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A Decade of Scholarly Research on Open Knowledge Graphs

https://github.com/davidlvxin/TransC

공식 코드

Differentiating Concepts and Instances for Knowledge Graph Embedding

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