https://github.com/Wangyuwen0627/GIP-Framework
공식 코드Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
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/XiaoxinHe/Graph-ViT-MLPMixer
공식 코드https://github.com/Yangyeeee/SGAT
공식 코드https://github.com/YuGuangWang/FGT
공식 코드Decimated Framelet System on Graphs and Fast G-Framelet Transforms
https://github.com/YuGuangWang/HaarPool
공식 코드https://github.com/YuGuangWang/PAN
공식 코드Path Integral Based Convolution and Pooling for Graph Neural Networks
https://github.com/YuehuaZhu/ProxyGML
공식 코드Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
https://github.com/YuxiangRen/Label-Contrastive-Coding-based-Graph-Neural-Network-for-Graph-Classification-
공식 코드Label Contrastive Coding based Graph Neural Network for Graph Classification
https://github.com/aalto-quml/pipe
공식 코드https://github.com/aidos-lab/filtration_surfaces
공식 코드https://github.com/aimat-lab/ml4pxrds
공식 코드https://github.com/aister2020/KDDCUP_2020_AutoGraph_1st_Place
공식 코드AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
https://github.com/aledinve/static-gnn
공식 코드On the approximation capability of GNNs in node classification/regression tasks
https://github.com/alexduvalinho/hoscpool
공식 코드Higher-order Clustering and Pooling for Graph Neural Networks
https://github.com/alge24/DyGNN
공식 코드https://github.com/alirezadizaji/graphroar
공식 코드On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
https://github.com/amirkhas/GraphMemoryNet
공식 코드https://github.com/andyweizhao/spd4gnns
공식 코드Modeling Graphs Beyond Hyperbolic: Graph Neural Networks in Symmetric Positive Definite Matrices
https://github.com/anwar-said/esgea
공식 코드Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
https://github.com/arashrasti96/gstam
공식 코드GSTAM: Efficient Graph Distillation with Structural Attention-Matching
https://github.com/arichibald/grafourierformer
공식 코드Graph Fourier Transformer with Structure-Frequency Information
https://github.com/ariesoeteman/hegnn
공식 코드Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
https://github.com/artessay/ragraph
공식 코드RAGraph: A General Retrieval-Augmented Graph Learning Framework
https://github.com/asfeng/kergnns
공식 코드KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
https://github.com/automl-research/pas
공식 코드https://github.com/automl-research/pas-ogb
공식 코드https://github.com/balcilar/Spectral-Designed-Graph-Convolutions
공식 코드Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
https://github.com/bardhprenkaj/gist
공식 코드https://github.com/bayer-science-for-a-better-life/phc-gnn
공식 코드Parameterized Hypercomplex Graph Neural Networks for Graph Classification
https://github.com/benedekrozemberczki/FEATHER
공식 코드https://github.com/benedekrozemberczki/datasets
공식 코드Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
https://github.com/benedekrozemberczki/karateclub
공식 코드Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
https://github.com/benedekrozemberczki/littleballoffur
공식 코드https://github.com/benedekrozemberczki/shapley
공식 코드https://github.com/benfinkelshtein/CoveredForests
공식 코드Covered Forest: Fine-grained generalization analysis of graph neural networks
https://github.com/bengutteridge/drew
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.