https://github.com/siddhant-doshi/spin
공식 코드Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification
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/sigeisler/s2gnn
공식 코드https://github.com/siml3/hl-net
공식 코드HL-Net: Heterophily Learning Network for Scene Graph Generation
https://github.com/simonzhang00/GraphExtendedFiltrationLearning
공식 코드https://github.com/skeletondyh/gred
공식 코드Recurrent Distance Filtering for Graph Representation Learning
https://github.com/snap-stanford/pretrain-gnns/
공식 코드https://github.com/snudatalab/AugWard
공식 코드AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
https://github.com/snudatalab/GraphAug
공식 코드Model-Agnostic Augmentation for Accurate Graph Classification
https://github.com/songw-sw/faith
공식 코드FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
https://github.com/spark483/the-pwlr-graph-representation
공식 코드https://github.com/submissionconff/g2gnn
공식 코드Imbalanced Graph Classification via Graph-of-Graph Neural Networks
https://github.com/sukwonyun/s-mixup
공식 코드https://github.com/sunjss/N2
공식 코드https://github.com/takeuchi-lab/Learning-Interpretable-Metric-between-Graphs
공식 코드https://github.com/taowen0309/ttg-nn
공식 코드https://github.com/tech-srl/how_attentive_are_gats
공식 코드https://github.com/thudm/graphmae
공식 코드https://github.com/thunlp/opengcl
공식 코드https://github.com/tianyao-aka/mugsi
공식 코드MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
https://github.com/tiongleslie/crystal-structure-classification
공식 코드https://github.com/tkipf/pygcn
공식 코드Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/relational-gcn
공식 코드https://github.com/toenshoff/CRaWl
공식 코드Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
https://github.com/toenshoff/lrgb
공식 코드Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/topogclaaai24/topogcl
공식 코드https://github.com/topologicalpooling/TopologicalPool
공식 코드https://github.com/tpfi22/spectral-and-combinatorial
공식 코드Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
https://github.com/tri-gnn/tri-gnn
공식 코드https://github.com/trustworthygnn/mia-gnn
공식 코드Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
https://github.com/twistedcubic/copt
공식 코드https://github.com/twitter-research/cwn
공식 코드https://github.com/ucbrise/graphtrans
공식 코드Representing Long-Range Context for Graph Neural Networks with Global Attention
https://github.com/ujeong1/IEEEBigdata22_HGFND
공식 코드Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
https://github.com/uta-smile/HGCN
공식 코드https://github.com/venomouscyanide/s3grl
공식 코드Simplifying Subgraph Representation Learning for Scalable Link Prediction
https://github.com/venomouscyanide/s3grl_ogb
공식 코드Simplifying Subgraph Representation Learning for Scalable Link Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.