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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/nslab-cuk/unified-graph-transformer

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Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity

https://github.com/nus-hpc-ai-lab/ctrl

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Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching

https://github.com/nustm/cpnc

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Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering

https://github.com/oliver-lloyd/kge_param_sens

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Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality

https://github.com/opspai/grlia

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Graph-based Incident Aggregation for Large-Scale Online Service Systems

https://github.com/otamio/kga

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Augmenting Knowledge Graphs for Better Link Prediction

https://github.com/palash1992/DynamicGEM

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dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning

https://github.com/paramita08/wikidata_bias_websci_2023

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Diversity matters: Robustness of bias measurements in Wikidata

https://github.com/pat-jj/KG-FIT

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KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge

https://github.com/pbaghershahi/TGCN

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Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition

https://github.com/pbielak/graph-barlow-twins

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Graph Barlow Twins: A self-supervised representation learning framework for graphs

https://github.com/pcy1302/TransCF

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Collaborative Translational Metric Learning

https://github.com/peiyance/transgnn-torch

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TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

https://github.com/pengyanhui/LineaRE

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LineaRE: Simple but Powerful Knowledge Graph Embedding for Link Prediction

https://github.com/perubhardwaj/inferenceattack

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Poisoning Knowledge Graph Embeddings via Relation Inference Patterns

https://github.com/peyman-bi/sattle

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Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction

https://github.com/phd-lanyu/gtc

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GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation

https://github.com/phd-lanyu/mhnf

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MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning

https://github.com/pku-dair/nafs

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NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning

https://github.com/pkuyzy/TLC-GNN

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Neural Approximation of Graph Topological Features

https://github.com/pkuyzy/cbgnn

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Cycle Representation Learning for Inductive Relation Prediction

https://github.com/pkuyzy/cyclenet

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Cycle Invariant Positional Encoding for Graph Representation Learning

https://github.com/plantc/GEMPE

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Massively Parallel Graph Drawing and Representation Learning

https://github.com/pykeen/benchmarking

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Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

https://github.com/pykeen/ilpc2022

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An Open Challenge for Inductive Link Prediction on Knowledge Graphs

https://github.com/pykeen/pykeen

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The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability

https://github.com/pykeen/ranking-metrics-manuscript

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A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs

https://github.com/qslim/pdf

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Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

https://github.com/rajathpatel23/joint-kge-fnet-lm

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On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling

https://github.com/rampasek/GraphGPS

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Recipe for a General, Powerful, Scalable Graph Transformer

https://github.com/renli1024/se-gnn

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How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

https://github.com/rezanmz/GL-Coarsener

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GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solvers

https://github.com/rezanmz/smgrl

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SMGRL: Scalable Multi-resolution Graph Representation Learning

https://github.com/richard-peng-xia/HGCLIP

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HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

https://github.com/riemanngraph/motifrgc

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Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning

https://github.com/ringbdstack/hyperimba

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Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

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