https://github.com/A4Bio/GraphsGPT
공식 코드A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
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/AGH-ML-and-Chemoinformatics-Group/peptides_molecular_fingerprints_classification
공식 코드Molecular Fingerprints Are Strong Models for Peptide Function Prediction
https://github.com/AdrianArnaiz/DiffWire
공식 코드https://github.com/AkiraJM/LCSGK
공식 코드LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric Space
https://github.com/Axeln78/Transferability-of-spectral-gnns
공식 코드An Experimental Study of the Transferability of Spectral Graph Networks
https://github.com/BUAA-WJR/SO-ET
공식 코드Structural Optimization Makes Graph Classification Simpler and Better
https://github.com/BorgwardtLab/WWL
공식 코드https://github.com/CODE-SUBMIT/Graph_Neighborhood_1
공식 코드https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network
공식 코드https://github.com/CS-SAIL/UQ-GNN-ROC
공식 코드https://github.com/CSU-TDA/PersistenceImages
공식 코드Persistence Images: A Stable Vector Representation of Persistent Homology
https://github.com/Chen-Cai-OSU/LDP
공식 코드A simple yet effective baseline for non-attributed graph classification
https://github.com/Chrisa142857/neuro_detour
공식 코드NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
https://github.com/Cortys/master-thesis
공식 코드https://github.com/Cqkkkkkk/DAGPrompT
공식 코드https://github.com/CrawlScript/tf_geometric
공식 코드Efficient Graph Deep Learning in TensorFlow with tf_geometric
https://github.com/DavideBuffelli/CliquePH
공식 코드https://github.com/DavideBuffelli/SizeShiftReg
공식 코드SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
https://github.com/DongqiFu/DPPIN
공식 코드DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
https://github.com/EvanZhuang/vector-icl
공식 코드Vector-ICL: In-context Learning with Continuous Vector Representations
https://github.com/FilippoMB/Benchmark_dataset_for_graph_classification
공식 코드Spectral Clustering with Graph Neural Networks for Graph Pooling
https://github.com/FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
공식 코드Spectral Clustering with Graph Neural Networks for Graph Pooling
https://github.com/GGA23/GrokFormer
공식 코드https://github.com/GeorgeMLP/basis-invariance-synthetic-experiment
공식 코드Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
https://github.com/GraphCL/PyGCL
공식 코드https://github.com/HW-HaoWang/DHC-E
공식 코드https://github.com/HarrialX/GraphBackdoor
공식 코드https://github.com/Hermina/GOT
공식 코드https://github.com/HongyangGao/gunet
공식 코드https://github.com/IBM/IPC-graph-data
공식 코드IPC: A Benchmark Data Set for Learning with Graph-Structured Data
https://github.com/ICLab4DL/GNNBenchEffectiveness
공식 코드Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
https://github.com/JetRichardLee/PGNNCert
공식 코드https://github.com/JinheonBaek/GMT
공식 코드Accurate Learning of Graph Representations with Graph Multiset Pooling
https://github.com/Junyoungpark/CGS
공식 코드https://github.com/KaiGuo20/Ortho-GConv
공식 코드https://github.com/KangchengHou/gntk
공식 코드Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.