https://github.com/nur-ag/elene
공식 코드Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
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/opengraphxai/benchmarks
공식 코드https://github.com/opengsl/opengsl
공식 코드Motif-driven Subgraph Structure Learning for Graph Classification
https://github.com/paulmorio/geo2dr
공식 코드https://github.com/peiyance/transgnn-torch
공식 코드https://github.com/phtgraph/temporal_classification
공식 코드https://github.com/phython96/ARGNP
공식 코드https://github.com/pierreosselin/structureaware
공식 코드Structure-Aware Robustness Certificates for Graph Classification
https://github.com/pkrsn4/gnn-inductive-bias
공식 코드Graph Classification with GNNs: Optimisation, Representation and Inductive Bias
https://github.com/pku-ml/architecturemattersgcl
공식 코드Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
https://github.com/pku-ml/laplaciancanonization
공식 코드Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
https://github.com/psanch21/cores
공식 코드Improving the interpretability of GNN predictions through conformal-based graph sparsification
https://github.com/qcri/pdns-net
공식 코드PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning
https://github.com/qslim/gnn-spectrum
공식 코드A New Perspective on the Effects of Spectrum in Graph Neural Networks
https://github.com/radoslav11/sp-mpnn
공식 코드https://github.com/rampasek/GraphGPS
공식 코드https://github.com/rinnesz/gmae
공식 코드https://github.com/rongrongma/cos-gnn
공식 코드Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
https://github.com/rongrongma/mosgnn
공식 코드Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
https://github.com/roth-andreas/pprgnn
공식 코드Transforming PageRank into an Infinite-Depth Graph Neural Network
https://github.com/ruizhang-ai/SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems
공식 코드Detecting Beneficial Feature Interactions for Recommender Systems
https://github.com/rusty1s/pytorch_geometric
공식 코드SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/safe-graph/GNN-FakeNews
공식 코드https://github.com/salvatorecalderaro/geco_explainer
공식 코드https://github.com/samirmoustafa/mixq
공식 코드Efficient Mixed Precision Quantization in Graph Neural Networks
https://github.com/samsungsds-research-papers/lgvr
공식 코드Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning
https://github.com/samyu0304/vgib
공식 코드Improving Subgraph Recognition with Variational Graph Information Bottleneck
https://github.com/sawlani/a-doge
공식 코드https://github.com/sds-lab/rot-pooling
공식 코드Regularized Optimal Transport Layers for Generalized Global Pooling Operations
https://github.com/seaocn/egc2
공식 코드EGC2: Enhanced Graph Classification with Easy Graph Compression
https://github.com/seongjunyun/neo_gnns
공식 코드Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
https://github.com/shamim-hussain/egt
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_pytorch
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_triangular
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/sherylhyx/gnn_mf_ge
공식 코드Generalization Error of Graph Neural Networks in the Mean-field Regime
https://github.com/shweta-jacob/ssnp
공식 코드Stochastic Subgraph Neighborhood Pooling for Subgraph Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.