https://github.com/jindi-tju/CGMN
공식 코드CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Papers with Code 아카이브
Graph Classification
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Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jjzhou012/I2BGNN
공식 코드https://github.com/jordan7186/page
공식 코드PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
https://github.com/jrwnter/pigvae
공식 코드Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning
https://github.com/jugechengzi/Rationalization-N2R
공식 코드Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
https://github.com/jugechengzi/rationalization-a2i
공식 코드Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
https://github.com/junxia97/simgrace
공식 코드Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
https://github.com/jw9730/lps
공식 코드Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
https://github.com/jw9730/tokengt
공식 코드https://github.com/jwu4sml/DEMO-Net
공식 코드DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
https://github.com/jwzhanggy/SEG-BERT
공식 코드https://github.com/karl-zhao/benchmarking-gnns-pyg
공식 코드https://github.com/karolismart/agentnet
공식 코드https://github.com/karolismart/dropgnn
공식 코드DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
https://github.com/kavehhassani/metagrl
공식 코드https://github.com/kavehhassani/mvgrl
공식 코드https://github.com/kedar2/FoSR
공식 코드FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
https://github.com/kleincup/MGMN
공식 코드Multilevel Graph Matching Networks for Deep Graph Similarity Learning
https://github.com/kuandeng/LightGCN
공식 코드LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
https://github.com/kvignesh1420/rlap
공식 코드Randomized Schur Complement Views for Graph Contrastive Learning
https://github.com/lancopku/well-classified-examples-are-underestimated
공식 코드Well-classified Examples are Underestimated in Classification with Deep Neural Networks
https://github.com/largeapp/tsen
공식 코드Transformer and Snowball Graph Convolution Learning for Brain functional network Classification
https://github.com/lars-research/lrgnn
공식 코드Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
https://github.com/lechengkong/oneforall
공식 코드One for All: Towards Training One Graph Model for All Classification Tasks
https://github.com/leemingo/tigt
공식 코드https://github.com/leichen2018/GNN-Substructure-Counting
공식 코드https://github.com/leichen2018/GNN_vs_GAMLP
공식 코드https://github.com/leichen2018/Ring-GNN
공식 코드On the equivalence between graph isomorphism testing and function approximation with GNNs
https://github.com/lenscloth/grpe
공식 코드https://github.com/liamma/grit
공식 코드Graph Inductive Biases in Transformers without Message Passing
https://github.com/lightaime/deep_gcns
공식 코드https://github.com/limaosen0/GXN
공식 코드https://github.com/lingxiaoshawn/kcsetgnn
공식 코드https://github.com/liu-jc/mgnni
공식 코드MGNNI: Multiscale Graph Neural Networks with Implicit Layers
https://github.com/liu-jingzhe/graph-scaling-laws
공식 코드https://github.com/liuchuang0059/gradformer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.