https://github.com/liuwenfeng93/LPD-GCN
공식 코드Locality Preserving Dense Graph Convolutional Networks with Graph Context-Aware Node Representations
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/llirarry/gfm-code
공식 코드Neural Network Graph Similarity Computation Based on Graph Fusion
https://github.com/lrnzgiusti/can
공식 코드https://github.com/lspongebobjh/virgo_icml2023
공식 코드https://github.com/luciatesta97/generalized-simplicial-attention-neural-networks
공식 코드https://github.com/lukecavabarrett/pna
공식 코드https://github.com/lumia-group/graphparsingnetworks
공식 코드https://github.com/luoyk1999/hdse
공식 코드Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
https://github.com/maha-93/Clustering-Testing-Networks
공식 코드Graphon based Clustering and Testing of Networks: Algorithms and Theory
https://github.com/malllabiisc/ASAP
공식 코드ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
https://github.com/malllabiisc/CompGCN
공식 코드Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/mandeep-rathee/bagel-benchmark
공식 코드BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
https://github.com/manhtuando97/two-stage-gnn
공식 코드Two-stage Training of Graph Neural Networks for Graph Classification
https://github.com/manoskary/musgconv
공식 코드Perception-Inspired Graph Convolution for Music Understanding Tasks
https://github.com/maowenyu-11/infoigl
공식 코드Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
https://github.com/marcessz/complex-networks
공식 코드https://github.com/matenure/OTCoarsening
공식 코드https://github.com/mayabechlerspeicher/Graph_Neural_Networks_Overfit_Graphs
공식 코드https://github.com/mayabechlerspeicher/tree-g
공식 코드https://github.com/meltzerpete/PiNet
공식 코드https://github.com/mertkosan/GCFExplainer
공식 코드https://github.com/metaoblivion/bandit_gnn_attack
공식 코드https://github.com/mewispool/mewispool
공식 코드Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
https://github.com/mn51/graphmad
공식 코드GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
https://github.com/mocherson/Exp_GNN
공식 코드https://github.com/motivationss/igs
공식 코드https://github.com/muhanzhang/nestedgnn
공식 코드https://github.com/mys007/ecc
공식 코드Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
https://github.com/name-is-what/sego
공식 코드Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
https://github.com/navid-naderi/WEGL
공식 코드https://github.com/nd7141/AWE
공식 코드https://github.com/nd7141/iso_bias
공식 코드https://github.com/nec-research/graph-sum-product-networks
공식 코드Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
https://github.com/networkslab/ckgconv
공식 코드https://github.com/nickskyyy/lcc-for-gc
공식 코드An Efficient Loop and Clique Coarsening Algorithm for Graph Classification
https://github.com/nslab-cuk/unified-graph-transformer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.