https://github.com/uestclbh/pc-conv
공식 코드PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
Papers with Code 아카이브
Node Classification
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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/urchade/grapher
공식 코드GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
https://github.com/urielsinger/tNodeEmbed
공식 코드https://github.com/utcsilab/ESI_HGE
공식 코드Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference
https://github.com/vandijklab/scGAT
공식 코드Disease State Prediction From Single-Cell Data Using Graph Attention Networks
https://github.com/vasile-paskardlgm/cpf
공식 코드Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
https://github.com/vasile-paskardlgm/tfgnn
공식 코드Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
https://github.com/ventr1c/infattack
공식 코드https://github.com/ventr1c/ugba
공식 코드https://github.com/vermouthdky/simteg
공식 코드SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
https://github.com/victorchen96/renode
공식 코드Topology-Imbalance Learning for Semi-Supervised Node Classification
https://github.com/vijaydwivedi75/lrgb
공식 코드https://github.com/vikasverma1077/GraphMix
공식 코드GraphMix: Improved Training of GNNs for Semi-Supervised Learning
https://github.com/viktoraxelsen/tgsl
공식 코드Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
https://github.com/virresh/viking
공식 코드Adversarial Attack on Network Embeddings via Supervised Network Poisoning
https://github.com/virtuosoresearch/boosting-multitask-learning-on-graphs
공식 코드Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
https://github.com/vita-group/large_scale_gcn_benchmarking
공식 코드A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
https://github.com/vitaminzl/hqa-gae
공식 코드Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
https://github.com/viz27/secomm
공식 코드Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network
https://github.com/vmtenorio/neighborhoodgf
공식 코드A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters
https://github.com/vuptran/graph-representation-learning
공식 코드https://github.com/vwz/AGE
공식 코드https://github.com/w-rudder/tea-glm
공식 코드LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/wang-chen/LGL
공식 코드https://github.com/wang-chen/lgl-action-recognition
공식 코드https://github.com/wangxiaotang0906/degta
공식 코드https://github.com/wangxiaotang0906/unigap
공식 코드https://github.com/wangyu0627/gc-hgnn
공식 코드https://github.com/wattpad/gatas
공식 코드https://github.com/weiganglu/agmixup
공식 코드AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
https://github.com/weiganglu/nodemixup
공식 코드NodeMixup: Tackling Under-Reaching for Graph Neural Networks
https://github.com/weiganglu/skipnode
공식 코드SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
https://github.com/weihua916/powerful-gnns
공식 코드https://github.com/wenkelf/copt
공식 코드Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
https://github.com/wenlun-zhang/lisa
공식 코드LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection
https://github.com/wentao-xu/shgnn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.