https://github.com/tbh-98/hypergraph-mlp
공식 코드Hypergraph-MLP: Learning on Hypergraphs without Message Passing
Papers with Code 아카이브
Node Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 138
- 보존된 평가 기록
- 1,793
Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.
Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.
<span style="color:grey; opacity: 0.6">( Image credit: Fast Graph Representation Learning With PyTorch Geometric )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tedzhouhk/GCNP
공식 코드Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
https://github.com/tfahim15/had2024
공식 코드https://github.com/thisisforreview/svd-gcn
공식 코드https://github.com/thiviyanT/torch-rgcn
공식 코드https://github.com/thomas-wyh/b-attention
공식 코드Robust Graph Structure Learning via Multiple Statistical Tests
https://github.com/thudm/crgnn
공식 코드SCR: Training Graph Neural Networks with Consistency Regularization
https://github.com/thudm/graphmae
공식 코드https://github.com/thudm/hgb
공식 코드https://github.com/thudm/scr
공식 코드SCR: Training Graph Neural Networks with Consistency Regularization
https://github.com/thuwzy/Cluster-Attack
공식 코드Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
https://github.com/tianbian95/pi-gnn
공식 코드Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions
https://github.com/tianqi-py/agale
공식 코드AGALE: A Graph-Aware Continual Learning Evaluation Framework
https://github.com/tianqi-py/mlgnc
공식 코드https://github.com/tianxiangzhao/disambiguatedgnn
공식 코드Disambiguated Node Classification with Graph Neural Networks
https://github.com/tianyao-aka/mugsi
공식 코드MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
https://github.com/timelovercc/caf-gnn
공식 코드https://github.com/tk-rusch/gradientgating
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/lrgb
공식 코드Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/toinesayan/node-classification-and-label-dependencies
공식 코드https://github.com/tonyauyeung/local2gae2global
공식 코드L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
https://github.com/topgunlcs98/scrl
공식 코드Self-supervised Consensus Representation Learning for Attributed Graph
https://github.com/trais-lab/llm-structured-data
공식 코드Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
https://github.com/tri-gnn/tri-gnn
공식 코드https://github.com/tsy935/graphs4mer
공식 코드Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
https://github.com/tuanh23/e2eg
공식 코드E2EG: End-to-End Node Classification Using Graph Topology and Text-based Node Attributes
https://github.com/twelfth-star/LinkGPT
공식 코드LinkGPT: Teaching Large Language Models To Predict Missing Links
https://github.com/twitter-research/feature-propagation
공식 코드https://github.com/twitter-research/neural-sheaf-diffusion
공식 코드Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
https://github.com/twitter-research/sign
공식 코드https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding
공식 코드Adversarial Privacy Preserving Graph Embedding against Inference Attack
https://github.com/ubc-systopia/CATWalk
공식 코드https://github.com/ubc-systopia/DP-AdamBC
공식 코드DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)
https://github.com/uestc-lese/spgrl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.