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/basiralab/Reproducible-Generative-Learning

Graph Attention Networks

https://github.com/basiralab/reproduciblefedgnn

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/bcmi220/esc4nmt

Attention Is All You Need

https://github.com/bdusell/stack-attention

Attention Is All You Need

https://github.com/benedekrozemberczki/AttentionWalk

Watch Your Step: Learning Node Embeddings via Graph Attention

https://github.com/benedekrozemberczki/BANE

https://github.com/benedekrozemberczki/ClusterGCN

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

https://github.com/benedekrozemberczki/Splitter

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

https://github.com/benedekrozemberczki/karateclub

node2vec: Scalable Feature Learning for Networks

https://github.com/benedekrozemberczki/pytorch_geometric_temporal

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

https://github.com/benjamintaiwo/Attention

Attention Is All You Need

https://github.com/berlincho/RGCN-pytorch

Modeling Relational Data with Graph Convolutional Networks

https://github.com/bhavitvyamalik/bert

Attention Is All You Need

https://github.com/bi-graph/emgraph

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

https://github.com/binhetech/bert-application

Attention Is All You Need

https://github.com/bkoch4142/attention-is-all-you-need-paper

Attention Is All You Need

https://github.com/blueberryc/pyGAT

Graph Attention Networks

https://github.com/blueghostyi/id-grec

Graph Convolutional Matrix Completion

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

Attention Is All You Need

https://github.com/blzhu0823/pathfusion

MMKG: Multi-Modal Knowledge Graphs

https://github.com/boom85423/hello_SHINE

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

https://github.com/bowu/GraphSearch

Graph Attention Networks

https://github.com/brainsqueeze/text2vec

Attention Is All You Need

https://github.com/brandontrabucco/scaled_dot_product_attention

Attention Is All You Need

https://github.com/briangodwinlim/sir-gcn

How Attentive are Graph Attention Networks?

https://github.com/brightmart/bert_customized

Attention Is All You Need

https://github.com/brightmart/bert_original

Attention Is All You Need

https://github.com/bsantraigi/TA_TransE

Learning Sequence Encoders for Temporal Knowledge Graph Completion

https://github.com/bshantam97/Attention_Based_Networks

Attention Is All You Need

https://github.com/bytedance/neurst

Attention Is All You Need

https://github.com/bzhangGo/zero

Attention Is All You Need

https://github.com/cadia-lvl/fastspeech2

Attention Is All You Need

https://github.com/cahya-wirawan/indonesian-language-models

Attention Is All You Need

https://github.com/calciver/Graph-Attention-Networks

Graph Attention Networks

https://github.com/camlaedtke/NLP

Attention Is All You Need

https://github.com/caoyujiALgLM/NLP

Attention Is All You Need

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