https://github.com/kaize0409/GPN
공식 코드Graph Prototypical Networks for Few-shot Learning on Attributed Networks
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/karl-zhao/benchmarking-gnns-pyg
공식 코드https://github.com/kbogas/PAM_BoP
공식 코드From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
https://github.com/kgrl2021/submission-one
공식 코드A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
https://github.com/kimsu55/begin
공식 코드Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
https://github.com/klicperajo/gdc
공식 코드https://github.com/klicperajo/ppnp
공식 코드Predict then Propagate: Graph Neural Networks meet Personalized PageRank
https://github.com/ksadowski13/PCAPass
공식 코드Dimensionality Reduction Meets Message Passing for Graph Node Embeddings
https://github.com/kswoo97/hypeboy
공식 코드HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
https://github.com/kunzhan/MTGCN
공식 코드https://github.com/kvignesh1420/rlap
공식 코드Randomized Schur Complement Views for Graph Contrastive Learning
https://github.com/kylemin/SPELL
공식 코드Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection
https://github.com/l-wd/lgmformer
공식 코드Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
https://github.com/laclauc/fairness-graph
공식 코드https://github.com/lars-research/lrgnn
공식 코드Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
https://github.com/leduckhai/wav2graph
공식 코드wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech
https://github.com/leemingo/tigt
공식 코드https://github.com/lehaifeng/T-GCN
공식 코드Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations
https://github.com/leirunlin/evennet
공식 코드EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
https://github.com/lemmonation/APNE
공식 코드Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
https://github.com/lemmonation/G2-EMF
공식 코드Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
https://github.com/leonresearch/city-networks
공식 코드https://github.com/leoribeiro/struc2vec
공식 코드struc2vec: Learning Node Representations from Structural Identity
https://github.com/lequanlin/gnn-diff
공식 코드Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
https://github.com/lgalke/lifelong-learning
공식 코드Lifelong Learning of Graph Neural Networks for Open-World Node Classification
https://github.com/li1neo/lan
공식 코드LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection
https://github.com/liamma/grit
공식 코드Graph Inductive Biases in Transformers without Message Passing
https://github.com/lightaime/deep_gcns
공식 코드https://github.com/lightaime/deep_gcns_torch
공식 코드https://github.com/linchen-65/restruct
공식 코드Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network
https://github.com/liqimai/DSGC
공식 코드https://github.com/litong99/xpath
공식 코드Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network
https://github.com/liu-jc/lscale
공식 코드LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
https://github.com/liu-jc/mgnni
공식 코드MGNNI: Multiscale Graph Neural Networks with Implicit Layers
https://github.com/liu-jingzhe/graph-scaling-laws
공식 코드https://github.com/liu-yushan/calgnn
공식 코드On Calibration of Graph Neural Networks for Node Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.