https://github.com/dmlc/dgl/tree/master/examples/pytorch/cluster_gcn
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Papers with Code 아카이브
Link Prediction
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- 하위 태스크
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- 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/dmlc/dgl/tree/master/examples/pytorch/compGCN
Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/dmlc/dgl/tree/master/examples/pytorch/evolveGCN
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gnn_explainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec
https://github.com/dmlc/dgl/tree/master/examples/pytorch/node2vec
https://github.com/dmlc/dgl/tree/master/examples/pytorch/ogb_lsc
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero
https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal
https://github.com/dmlc/dgl/tree/master/examples/pytorch/vgae
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn
https://github.com/dmlc/gluon-nlp
https://github.com/dodo47/cyberml
Machine learning on knowledge graphs for context-aware security monitoring
https://github.com/doujiang-zheng/dynamic-preference-structure
https://github.com/dqqcasia/st
https://github.com/dreamgonfly/Transformer-pytorch
https://github.com/dreamnotover/english_chinese_machine_translation_baseline
https://github.com/drewwilimitis/hyperbolic-learning
Poincaré Embeddings for Learning Hierarchical Representations
https://github.com/drewwilimitis/poincare
Poincaré Embeddings for Learning Hierarchical Representations
https://github.com/drumpt/Transformer
https://github.com/ds4dm/sGat
https://github.com/ds4dm/sparse-gcn
https://github.com/duong18/BiasedWalk
BiasedWalk: Biased Sampling for Representation Learning on Graphs
https://github.com/duyunshu/bert-sentiment-analysis
https://github.com/dyq0811/EEG-Transformer-seq2seq
https://github.com/dzb1998/pyGAT
https://github.com/dzqjorking/transpose
https://github.com/edwarddavis1/universal_dynamic_embedding_with_testing
https://github.com/eleanorye00/Gamifier
https://github.com/elichienxD/SAGN_with_SLE
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
https://github.com/elichienxD/deep_gcns_torch
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
https://github.com/eliorc/node2vec
https://github.com/ellisalicante/StructuralGroupUnfairness
Structural Group Unfairness: Measurement and Mitigation by means of the Effective Resistance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.