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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/vermaMachineLearning/Graph-Capsule-CNN-Networks
공식 코드https://github.com/victorzxy/graphac
공식 코드Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
https://github.com/vijaydwivedi75/lrgb
공식 코드https://github.com/virtuosoresearch/tree-data-augmentation
공식 코드https://github.com/walidgeuttala/Synthetic-Benchmark-for-Graph-Classification
공식 코드https://github.com/wang-chen/LGL
공식 코드https://github.com/wang-chen/lgl-action-recognition
공식 코드https://github.com/wangxiaotang0906/degta
공식 코드https://github.com/weihua916/powerful-gnns
공식 코드https://github.com/wenkelf/copt
공식 코드Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
https://github.com/whuchuang/mid
공식 코드On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
https://github.com/williamleif/GraphSAGE
공식 코드https://github.com/wokas36/structposgssl
공식 코드Graph Self-Supervised Learning with Learnable Structural and Positional Encodings
https://github.com/woodcutter1998/gdgnn
공식 코드Geodesic Graph Neural Network for Efficient Graph Representation Learning
https://github.com/wu-junran/hierarchicalreporting
공식 코드https://github.com/wu-junran/sega
공식 코드SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
https://github.com/wu-junran/sep
공식 코드https://github.com/wuyucheng2002/ctaug
공식 코드https://github.com/wuyxin/dir-gnn
공식 코드https://github.com/wuyxin/falcon-glfrontiers
공식 코드https://github.com/xiangwang1223/reinforced_causal_explainer
공식 코드https://github.com/xiaoxinhe/g-retriever
공식 코드G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
https://github.com/xingchenwan/grabnel
공식 코드Adversarial Attacks on Graph Classification via Bayesian Optimisation
https://github.com/xtLyu/CrossBA
공식 코드Cross-Context Backdoor Attacks against Graph Prompt Learning
https://github.com/xueqic/topoedge
공식 코드Edge Classification on Graphs: New Directions in Topological Imbalance
https://github.com/ychen-stat-ml/gw-graph-coarsening
공식 코드A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
https://github.com/yeweiysh/DeepMap
공식 코드Learning Deep Graph Representations via Convolutional Neural Networks
https://github.com/yeweiysh/ihgnn
공식 코드Incorporating Heterophily into Graph Neural Networks for Graph Classification
https://github.com/yeweiysh/mpg
공식 코드Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
https://github.com/yongduosui/aia
공식 코드Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
https://github.com/yongduosui/cal
공식 코드Causal Attention for Interpretable and Generalizable Graph Classification
https://github.com/yongduosui/uil
공식 코드A Unified Invariant Learning Framework for Graph Classification
https://github.com/youjibiying/HERALD
공식 코드https://github.com/youjibiying/gtot-tuning
공식 코드Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
https://github.com/yqian108/inference-of-kronecker-graph
공식 코드Analysis and Approximate Inference of Large Random Kronecker Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.