Papers with Code 아카이브

Link Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/caoyujiALgLM/NLP-progress

Attention Is All You Need

https://github.com/cdhx/KBGAN_for_RotatE

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

https://github.com/cf020031308/mad-learning/blob/master/ogbl-ddi.py

Memory-Associated Differential Learning

https://github.com/chalothon/BERT_Practice

Attention Is All You Need

https://github.com/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction

Modeling Relational Data with Graph Convolutional Networks

https://github.com/chandu7077/mybert

Attention Is All You Need

https://github.com/changminwu/expandergnn

Benchmarking Graph Neural Networks

https://github.com/chanind/tensor-theorem-prover

End-to-End Differentiable Proving

https://github.com/chappers/graph-differential-pooling

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/charlie-xiao/embedding-visualization-test

GLEE: Geometric Laplacian Eigenmap Embedding

https://github.com/chatterjeeayan/upna

Inductive Representation Learning on Large Graphs

https://github.com/chen-xiong-yi/OwnBERT

Attention Is All You Need

https://github.com/chenfengzhao/gnnhls

Benchmarking Graph Neural Networks

https://github.com/chengchingwen/transformers.jl

Attention Is All You Need

https://github.com/chennnM/GCNII/tree/master/PyG/ogbn-arxiv

Simple and Deep Graph Convolutional Networks

https://github.com/cheungdaven/QuatE

Quaternion Knowledge Graph Embeddings

https://github.com/chfhf/GCC-paddle

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

https://github.com/chfhf/ngcf-paddle

Neural Graph Collaborative Filtering

https://github.com/chiayewken/bert-qa

Attention Is All You Need

https://github.com/chr26195/pmlp

MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

https://github.com/chrisleunglokhin/Capstone-BERT

Attention Is All You Need

https://github.com/christianspybrook/article_recommender

Attention Is All You Need

https://github.com/clementchadebec/benchmark_VAE

Hyperspherical Variational Auto-Encoders

https://github.com/cloudmcloudyo/capstone

Attention Is All You Need

https://github.com/cmantoux/sparse-low-rank-decomposition

Estimation of Simultaneously Sparse and Low Rank Matrices

https://github.com/cmavro/Graph-InfoClust-GIC

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

https://github.com/cmavro/HeMI

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

https://github.com/cmsflash/efficient-attention

Attention Is All You Need

https://github.com/coco60/bert-test

Attention Is All You Need

https://github.com/colorfulscoop/tfdlg

Attention Is All You Need

https://github.com/conceptofmind/LaMDA-pytorch

Attention Is All You Need

https://github.com/conceptofmind/lamda-rlhf-pytorch

Attention Is All You Need

https://github.com/conti748/wyze-rule-recommendation

FedRule: Federated Rule Recommendation System with Graph Neural Networks

https://github.com/coronazap/bert_client

Attention Is All You Need

https://github.com/cosmoquester/seq2seq

Attention Is All You Need

https://github.com/cospplay/bert-master

Attention Is All You Need

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