https://github.com/bgu-cs-vil/im-mpnn
공식 코드Improving the Effective Receptive Field of Message-Passing 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/bhoov/energy-transformer-jax
공식 코드https://github.com/bishengli0327/bsal
공식 코드BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction
https://github.com/bknyaz/graph_attention_pool
공식 코드Understanding Attention and Generalization in Graph Neural Networks
https://github.com/bluer555/KernelGCN
공식 코드Rethinking Kernel Methods for Node Representation Learning on Graphs
https://github.com/bojarlab/gifflar
공식 코드Higher-Order Message Passing for Glycan Representation Learning
https://github.com/borgwardtlab/motifiesta
공식 코드https://github.com/borgwardtlab/neuralwalker
공식 코드https://github.com/brcsomnath/fasttreeintegrator
공식 코드Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs
공식 코드https://github.com/bryanzhou008/hard_label_black_box_attack_gnn
공식 코드Hard Label Black Box Node Injection Attack on Graph Neural Networks
https://github.com/c-hofer/graph_filtration_learning
공식 코드https://github.com/caidao22/pnode
공식 코드https://github.com/cakcora/PeaceCorps
공식 코드Smart Vectorizations for Single and Multiparameter Persistence
https://github.com/carlo-abrate/Counterfactual-Explanations-for-Graph-Classification-Through-the-Lenses-of-Density
공식 코드Counterfactual Explanations for Graph Classification Through the Lenses of Density
https://github.com/carlosinator/support-graphs
공식 코드https://github.com/cborile/eval_lp_xai
공식 코드Evaluating Link Prediction Explanations for Graph Neural Networks
https://github.com/cedricvincentcuaz/GDL
공식 코드https://github.com/cedricvincentcuaz/TFGW
공식 코드Template based Graph Neural Network with Optimal Transport Distances
https://github.com/chauhanjatin10/GraphsFewShot
공식 코드Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
https://github.com/chen-cai-osu/mpnn-gt-connection
공식 코드https://github.com/chenjie20/SSCDL
공식 코드https://github.com/chennnM/GCNII
공식 코드https://github.com/chentingpc/gfn
공식 코드Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
https://github.com/chingyaoc/tmd
공식 코드Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
https://github.com/chinmay5/ms_ida
공식 코드https://github.com/christopherhojny/SCIP-MPNN
공식 코드Verifying message-passing neural networks via topology-based bounds tightening
https://github.com/chrsmrrs/k-gnn
공식 코드Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
https://github.com/chrsmrrs/sparsewl
공식 코드Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
https://github.com/chrsmrrs/tudataset
공식 코드TUDataset: A collection of benchmark datasets for learning with graphs
https://github.com/claying/GCKN
공식 코드https://github.com/code4paper-2024/code4paper
공식 코드Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
https://github.com/crisostomi/metric-few-shot-graph
공식 코드https://github.com/cs-sail/tensor-mv-gcl
공식 코드https://github.com/cshjin/ogw
공식 코드Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
https://github.com/cszhangzhen/HGP-SL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.