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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/dchang56/snomed_kge

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Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings

https://github.com/deceptrax123/jpeb-gssl

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Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning

https://github.com/deepmind/deepmind-research

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Large-scale graph representation learning with very deep GNNs and self-supervision

https://github.com/deepmind/deepmind-research/tree/master/wikigraphs

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WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

https://github.com/deepsteiner/deepsteiner

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Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem

https://github.com/deezer/recsys21-hlr

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Hierarchical Latent Relation Modeling for Collaborative Metric Learning

https://github.com/dellixx/tkbc-lie

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Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding

https://github.com/dellixx/transerr

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TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation

https://github.com/dice-group/Convolutional-Hypercomplex-Embeddings-for-Link-Prediction

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Convolutional Hypercomplex Embeddings for Link Prediction

https://github.com/dice-group/OOV-In-Link-Prediction

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Out-of-Vocabulary Entities in Link Prediction

https://github.com/dice-group/PYKE

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A Physical Embedding Model for Knowledge Graphs

https://github.com/dice-group/dice-embeddings

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Kronecker Decomposition for Knowledge Graph Embeddings

https://github.com/dice-group/universal_embeddings

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Universal Knowledge Graph Embeddings

https://github.com/didi/hetsann

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An Attention-based Graph Neural Network for Heterogeneous Structural Learning

https://github.com/diningphil/continual_learning_for_graphs

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Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification

https://github.com/diningphil/gnn-comparison

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A Fair Comparison of Graph Neural Networks for Graph Classification

https://github.com/diningphil/graph-mixture-density-networks

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Graph Mixture Density Networks

https://github.com/divelab/AIRS

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Complete and Efficient Graph Transformers for Crystal Material Property Prediction

https://github.com/divelab/DeeperGNN

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Towards Deeper Graph Neural Networks

https://github.com/divelab/sopool

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Second-Order Pooling for Graph Neural Networks

https://github.com/dsaatusu/mooper_grade_prediction

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Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

https://github.com/dshi3553usyd/frameless_graph_distillation

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Frameless Graph Knowledge Distillation

https://github.com/dwslab/jRDF2Vec

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RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

https://github.com/eXascaleInfolab/daor

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Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection

https://github.com/edisonleeeee/spikenet

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Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks

https://github.com/egordm/mgtcom

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MGTCOM: Community Detection in Multimodal Graphs

https://github.com/ejwww/tangnn

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TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning

https://github.com/ellenzhuwang/lakermap

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Contextualized Structural Self-supervised Learning for Ontology Matching

https://github.com/erfanloghmani/effect-of-loss-function-tbatching

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Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks

https://github.com/erfanloghmani/myket-android-application-market-dataset

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Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks

https://github.com/euranova/augment_to_interpret

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Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings

https://github.com/event-ahu/agcn_event_classification

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Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition

https://github.com/facebookresearch/ssl-relation-prediction

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Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

https://github.com/facebookresearch/taser-tgnn

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TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning

https://github.com/fanourakis/experimental-review-ea

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Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review

https://github.com/fdudsde/magic

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MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning

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