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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/fengshikun/unimap

공식 코드

UniMAP: Universal SMILES-Graph Representation Learning

https://github.com/g-ailab/mvge

공식 코드

Multi-View Graph Representation Learning Beyond Homophily

https://github.com/g-taxonomy-workgroup/gtaxogym

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Taxonomy of Benchmarks in Graph Representation Learning

https://github.com/gexinh/bpgt

공식 코드

Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification

https://github.com/ggoh29/simplicial-neural-network-benchmark

공식 코드

Simplicial Attention Networks

https://github.com/giannisnik/gnn-representations

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What Do GNNs Actually Learn? Towards Understanding their Representations

https://github.com/git-artes/cpd_rdpg

공식 코드

Online Change Point Detection for Weighted and Directed Random Dot Product Graphs

https://github.com/gmcmt/graph_prompt_extension

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Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs

https://github.com/gongchenghua/self-pro

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Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks

https://github.com/google/gcnn-survey-paper

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Machine Learning on Graphs: A Model and Comprehensive Taxonomy

https://github.com/graph-com/nlb

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Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling

https://github.com/graph-com/surel

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Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning

https://github.com/graph4ai/graph4nlp

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Graph Neural Networks for Natural Language Processing: A Survey

https://github.com/graphcore-research/kg-topology-toolbox

공식 코드

The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models

https://github.com/graphcore/distributed-kge-poplar

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BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion

https://github.com/graphmamba/gmn

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Graph Mamba: Towards Learning on Graphs with State Space Models

https://github.com/graphminddartmouth/graph-consistency

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Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks

https://github.com/graphml-lab-pwr/multiplex-fusion

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Representation learning in multiplex graphs: Where and how to fuse information?

https://github.com/graphpku/disgnn

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Is Distance Matrix Enough for Geometric Deep Learning?

https://github.com/gravins/tg-ode

공식 코드

Temporal Graph ODEs for Irregularly-Sampled Time Series

https://github.com/grigorisg9gr/polynomial_nets

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$Π-$nets: Deep Polynomial Neural Networks

https://github.com/grvndnz/parallel-fnn

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Towards Real-Time Temporal Graph Learning

https://github.com/gyshgx868/topo-ter

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Self-Supervised Graph Representation Learning via Topology Transformations

https://github.com/hang53/rcgrl

공식 코드

Robust Causal Graph Representation Learning against Confounding Effects

https://github.com/haoliu-cola/cola

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Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks

https://github.com/harryjo97/EHGNN

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Edge Representation Learning with Hypergraphs

https://github.com/hazdzz/ChebGibbsNet

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From ChebNet to ChebGibbsNet

https://github.com/hazdzz/MGC_v2

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MGC: A Complex-Valued Graph Convolutional Network for Directed Graphs

https://github.com/hdmmblz/multi-graph

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Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs

https://github.com/heyuntian/fairmile

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FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning

https://github.com/hgh0545/NA2

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Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning

https://github.com/hgh0545/graph-fraudster

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Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning

https://github.com/hku-medai/wsi-hgnn

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Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning

https://github.com/hkuds/diffkg

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DiffKG: Knowledge Graph Diffusion Model for Recommendation

https://github.com/hkust-knowcomp/dummynode4graphlearning

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Boosting Graph Structure Learning with Dummy Nodes

https://github.com/hkust-knowcomp/lmpnn

공식 코드

Logical Message Passing Networks with One-hop Inference on Atomic Formulas

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