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Link Prediction

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하위 태스크: 6
평가표: 79
보존된 평가 기록: 497

Link Prediction is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.

<span style="color:grey; opacity: 0.6">( Image credit: Inductive Representation Learning on Large Graphs )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/HazyResearch/hgcn

Poincaré Embeddings for Learning Hierarchical Representations

https://github.com/HeapHop30/graph-attention-nets

Graph Attention Networks

https://github.com/HeapHop30/hierarchical-pooling

Towards Sparse Hierarchical Graph Classifiers

https://github.com/Helsinki-NLP/shared-info

Attention Is All You Need

https://github.com/HeshamElAbd/SelfAttentionLangModel

Attention Is All You Need

https://github.com/HyTruongSon/InvariantGraphNetworks-Pytorch

The general theory of permutation equivarant neural networks and higher order graph variational encoders

https://github.com/HzcIrving/DeepLearning_PlayGround

Attention Is All You Need

https://github.com/IBM/pytorch-seq2seq

Attention Is All You Need

https://github.com/INK-USC/MHGRN

Modeling Relational Data with Graph Convolutional Networks

https://github.com/IOB-Muenster/TEclass2

Attention Is All You Need

https://github.com/IllinoisGraphBenchmark/IGB-Datasets

Inductive Representation Learning on Large Graphs

https://github.com/Indradyumna/PermGNN

Adversarial Permutation Guided Node Representations for Link Prediction

https://github.com/IrishCoffee/cudnnMultiHeadAttention

Attention Is All You Need

https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT

Attention Is All You Need

https://github.com/Izecson/saml-nmt

Attention Is All You Need

https://github.com/Izecson/sockeye-1.16.6

Attention Is All You Need

https://github.com/JNUpython/bert

Attention Is All You Need

https://github.com/JRC1995/Machine-Translation-Transformers

Attention Is All You Need

https://github.com/Jacfger/simple-stuffs

Logic Embeddings for Complex Query Answering

https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer

Attention Is All You Need

https://github.com/JeffJeffy/CS224W-OGB-DEA-JK

Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning

https://github.com/JeheonPark596/sentimentsurvival

Attention Is All You Need

https://github.com/JerryKwon/melon_plylst_continuation

Neural Collaborative Filtering vs. Matrix Factorization Revisited

https://github.com/JiabenLi/gae_paddlepaddle

Variational Graph Auto-Encoders

https://github.com/JiabenLi/rgcn_paddlepaddle

Modeling Relational Data with Graph Convolutional Networks

https://github.com/JohannLee1996/bert

Attention Is All You Need

https://github.com/JohnKurian/R2D2

Reasoning on Knowledge Graphs with Debate Dynamics

https://github.com/Joshuaoneheart/Fintech-Final-Report

Attention Is All You Need

https://github.com/Josiah96Zhang/Lemane

Learning Based Proximity Matrix Factorization for Node Embedding

https://github.com/JuliaSun623/VGAE_dgl

Variational Graph Auto-Encoders

https://github.com/KXY-PUBLIC/NASE

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

https://github.com/Kaimaoge/IGNNK

Graph Attention Networks

https://github.com/KanchiShimono/KGCN

Knowledge Graph Convolutional Networks for Recommender Systems

https://github.com/Katsumata420/bert_mlm

Attention Is All You Need

https://github.com/KeithGalli/pycon2020

Attention Is All You Need

https://github.com/Kennard123661/gcc

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

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