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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/yixinliu233/greet

공식 코드

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

https://github.com/yizhidamiaomiao/DoppelgangerGraph

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Generating a Doppelganger Graph: Resembling but Distinct

https://github.com/yizhilll/transher

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TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

https://github.com/yli131/braingrl

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Learning to Model the Relationship Between Brain Structural and Functional Connectomes

https://github.com/yongduosui/aia

공식 코드

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

https://github.com/ys-feng/DemoGraph

공식 코드

Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs

https://github.com/yuanbo2020/ergl

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Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification

https://github.com/yuanbo2020/hgrl

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Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning

https://github.com/yule-BUAA/R-HGNN

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Heterogeneous Graph Representation Learning with Relation Awareness

https://github.com/yunhui1998/gymjsp

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Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism

https://github.com/yur7nd/ptss

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Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision

https://github.com/yzjiao/Subg-Con

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Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

https://github.com/zahrag/GAIN

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Graph Representation Learning for Road Type Classification

https://github.com/zangzelin/paper_code_DMAGE/blob/main/README.md

공식 코드

Unsupervised Deep Manifold Attributed Graph Embedding

https://github.com/zcrabbit/vbpi-gnn

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Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks

https://github.com/zdh2292390/ParTrans-X

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Efficient Parallel Translating Embedding For Knowledge Graphs

https://github.com/zekarias-tilahun/goat

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Gossip and Attend: Context-Sensitive Graph Representation Learning

https://github.com/zekarias-tilahun/graph-surgeon

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Jointly Learnable Data Augmentations for Self-Supervised GNNs

https://github.com/zequnl/graphex

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Graphine: A Dataset for Graph-aware Terminology Definition Generation

https://github.com/zetayue/CPA

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Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation

https://github.com/zhangtia16/cgcl

공식 코드

CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations

https://github.com/zhangtia16/molgraph-lardo

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Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models

https://github.com/zhaoningyu1996/hm-gnn

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Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks

https://github.com/zhengchuanpan/increase

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INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

https://github.com/zhi520/nc-kge

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Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction

https://github.com/zhiqiangzhongddu/PM-HGNN

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Personalised Meta-path Generation for Heterogeneous GNNs

https://github.com/zhouyou-gu/ac-grl-wi-fi

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Graph Representation Learning for Contention and Interference Management in Wireless Networks

https://github.com/zhudanhao/deepe

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DeepE: a deep neural network for knowledge graph embedding

https://github.com/zihaowang/text_enhanced_kge

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Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces

https://github.com/zjukg/ankge

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Analogical Inference Enhanced Knowledge Graph Embedding

https://github.com/zjukg/cause

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CausE: Towards Causal Knowledge Graph Embedding

https://github.com/zjukg/comprehensive-study-over-relational-patterns

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A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

https://github.com/zjukg/earl

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Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

https://github.com/zjukg/mans

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Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding

https://github.com/zjukg/morse

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Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding

https://github.com/zjukg/neuralkg

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NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs

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