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Iterative Graph Self-Distillation

https://doi.org/10.1109/tkde.2023.3303885
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-540-45167-9_11openalex-snapshot
On Graph Kernels: Hardness Results and Efficient Alternatives
inferredcites →https://doi.org/10.1016/j.knosys.2022.109631openalex-snapshot
Graph Barlow Twins: A self-supervised representation learning framework for graphs
inferredcites →https://doi.org/10.1016/s2589-7500(20)30192-8openalex-snapshot
Artificial intelligence in COVID-19 drug repurposing
inferredcites →https://doi.org/10.1038/sdata.2014.22openalex-snapshot
Quantum chemistry structures and properties of 134 kilo molecules
inferredcites →https://doi.org/10.1103/physreve.69.026113openalex-snapshot
Finding and evaluating community structure in networks
inferredcites →https://doi.org/10.1109/acvmot.2005.107openalex-snapshot
Semi-Supervised Self-Training of Object Detection Models
inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-snapshot
FaceNet: A unified embedding for face recognition and clustering
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00975openalex-snapshot
Momentum Contrast for Unsupervised Visual Representation Learning
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01549openalex-snapshot
Exploring Simple Siamese Representation Learning
inferredcites →https://doi.org/10.1109/icdm.2005.132openalex-snapshot
Shortest-Path Kernels on Graphs
inferredcites →https://doi.org/10.1109/tkde.2021.3090866openalex-snapshot
Self-supervised Learning: Generative or Contrastive
inferredcites →https://doi.org/10.1109/tpami.2018.2858821openalex-snapshot
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
inferredcites →https://doi.org/10.1145/1961189.1961199openalex-snapshot
LIBSVM
inferredcites →https://doi.org/10.1145/2783258.2783417openalex-snapshot
Deep Graph Kernels
inferredcites →https://doi.org/10.1145/3394486.3403168openalex-snapshot
GCC
inferredcites →https://doi.org/10.1609/aaai.v35i12.17315openalex-snapshot
Data Augmentation for Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1707.05005openalex-snapshot
graph2vec: Learning Distributed Representations of Graphs
inferredcites →https://doi.org/10.48550/arxiv.1807.03748openalex-snapshot
Representation Learning with Contrastive Predictive Coding
inferredcites →https://doi.org/10.48550/arxiv.1907.13625openalex-snapshot
On Mutual Information Maximization for Representation Learning
inferredcites →https://doi.org/10.48550/arxiv.2002.05715openalex-snapshot
Self-Distillation Amplifies Regularization in Hilbert Space
inferredcites →https://doi.org/10.7551/mitpress/1120.003.0080openalex-snapshot
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4671157openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-032-25032-2_9openalex-snapshot
DGRCL: A Dynamic Contrastive Graph Learning Framework for Modeling Stock Market Evolution
inferred← citeshttps://doi.org/10.1016/j.artint.2024.104065openalex-snapshot
Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2024.109304openalex-snapshot
Heterogeneous data augmentation in graph contrastive learning for effective negative samples
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122385openalex-snapshot
Label-guided graph contrastive learning for semi-supervised node classification
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112271openalex-snapshot
SGCL: Semi-supervised Graph Contrastive Learning with confidence propagation algorithm for node classification
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.127710openalex-snapshot
Supervised contrastive learning for graph representation enhancement
inferred← citeshttps://doi.org/10.1109/tkde.2021.3131584openalex-snapshot
Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive
inferred← citeshttps://doi.org/10.1109/tkde.2022.3172903openalex-snapshot
Graph Self-Supervised Learning: A Survey
inferred← citeshttps://doi.org/10.1109/tkde.2024.3374773openalex-snapshot
A Teacher-Free Graph Knowledge Distillation Framework With Dual Self-Distillation
inferred← citeshttps://doi.org/10.1109/tkde.2026.3675811openalex-snapshot
Self-Knowledge Distillation and Its Application: A Survey
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3149997openalex-snapshot
Analyzing Heterogeneous Networks With Missing Attributes by Unsupervised Contrastive Learning
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3191086openalex-snapshot
Prototypical Graph Contrastive Learning
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3223018openalex-snapshot
On Representation Knowledge Distillation for Graph Neural Networks
inferred← citeshttps://doi.org/10.1145/3485447.3512208openalex-snapshot
Graph Communal Contrastive Learning
inferred← citeshttps://doi.org/10.1145/3637528.3671699openalex-snapshot
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
inferred← citeshttps://doi.org/10.1145/3711896.3736566openalex-snapshot
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
inferred← citeshttps://doi.org/10.1609/aaai.v36i7.20748openalex-snapshot
Simple Unsupervised Graph Representation Learning
inferred← citeshttps://doi.org/10.1609/aaai.v38i8.28693openalex-snapshot
Label Attentive Distillation for GNN-Based Graph Classification
inferred← citeshttps://doi.org/10.3390/app15073727openalex-snapshot
Knowledge Graph Construction: Extraction, Learning, and Evaluation
inferred← citeshttps://doi.org/10.48550/arxiv.2106.12484openalex-snapshot
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2108.10733openalex-snapshot
Graph Neural Networks: Methods, Applications, and Opportunities
inferred← citeshttps://doi.org/10.7717/peerj-cs.1659openalex-snapshot
BiMGCL: rumor detection via bi-directional multi-level graph contrastive learning
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