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Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling

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What this work says

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

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A survey on semi-supervised learning

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Multi-view graph convolutional networks with attention mechanism

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Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

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On embedding sequence correlations in attributed network for semi-supervised node classification

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Semi-supervised learning with mixed-order graph convolutional networks

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Curvature graph neural network

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Semi-supervised multi-view graph convolutional networks with application to webpage classification

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MGAT-ESM: Multi-channel graph attention neural network with event-sharing module for rumor detection

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Knowledge graph embedding by logical-default attention graph convolution neural network for link prediction

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A dynamical spatial-temporal graph neural network for traffic demand prediction

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Automatic image annotation via compact graph based semi-supervised learning

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Label propagation with structured graph learning for semi-supervised dimension reduction

inferredcites →https://doi.org/10.1016/j.neunet.2020.08.016openalex-snapshot

Learning image features with fewer labels using a semi-supervised deep convolutional network

inferredcites →https://doi.org/10.1016/j.neunet.2021.06.007openalex-snapshot

Label propagation via local geometry preserving for deep semi-supervised image recognition

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Deep semi-supervised learning via dynamic anchor graph embedding in latent space

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A Comprehensive Survey on Deep Graph Representation Learning

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Saliency propagation from simple to difficult

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2014 IEEE International Conference on Data Mining

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Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network

inferredcites →https://doi.org/10.1109/tip.2016.2563981openalex-snapshot

Multi-Modal Curriculum Learning for Semi-Supervised Image Classification

inferredcites →https://doi.org/10.1109/tkde.2023.3280859openalex-snapshot

Towards Semi-Supervised Universal Graph Classification

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Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval

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Label Propagation via Teaching-to-Learn and Learning-to-Teach

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A Comprehensive Survey on Graph Neural Networks

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Redundancy-Free Self-Supervised Relational Learning for Graph Clustering

inferredcites →https://doi.org/10.1109/tpami.2021.3080635openalex-snapshot

Line Graph Neural Networks for Link Prediction

inferredcites →https://doi.org/10.1145/1553374.1553380openalex-snapshot

Curriculum learning

inferredcites →https://doi.org/10.1145/3132734.3132735openalex-snapshot

Semi-supervised Distance Consistent Cross-modal Retrieval

inferredcites →https://doi.org/10.1145/3322122openalex-snapshot

Multi-Modal Curriculum Learning over Graphs

inferredcites →https://doi.org/10.1145/3394486.3403177openalex-snapshot

AM-GCN: Adaptive Multi-channel Graph Convolutional Networks

inferredcites →https://doi.org/10.1145/3404835.3463028openalex-snapshot

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

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Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification

inferredcites →https://doi.org/10.1145/3450316openalex-snapshot

Learning Graph Neural Networks with Positive and Unlabeled Nodes

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A Diffusion Model for POI Recommendation

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Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network

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DisenCite: Graph-Based Disentangled Representation Learning for Context-Specific Citation Generation

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Towards Fine-Grained Reasoning for Fake News Detection

inferredcites →https://doi.org/10.1609/aaai.v37i4.25559openalex-snapshot

GLCC: A General Framework for Graph-Level Clustering

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Collective Classification in Network Data

inferredcites →https://doi.org/10.24963/ijcai.2022/295openalex-snapshot

TGNN: A Joint Semi-supervised Framework for Graph-level Classification

inferredcites →https://doi.org/10.48550/arxiv.2010.13902openalex-snapshot

Graph Contrastive Learning with Augmentations

inferredcites →https://doi.org/10.5555/1248547.1248632openalex-snapshot

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

What points at this work

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

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Linear projection fused graph-based semi-supervised learning on multi-view data

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GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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Evidence-guided learning against noisy and sparse labels on graphs

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Hybrid deep learning approaches for photovoltaic fault detection: From supervised CNNs to self-supervised masked autoencoders

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COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

inferred← citeshttps://doi.org/10.1016/j.ins.2024.121671openalex-snapshot

Consensus-based iterative meta-pseudo-labeling for deep semi-supervised learning

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A multi-channel curriculum learning framework for few labeled node classification

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112107openalex-snapshot

GL-GNN: Graph learning via the network of graphs

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.knosys.2024.112389openalex-snapshot

Adaptive-propagating heterophilous graph convolutional network

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Boosting semi-supervised regressor via confidence-weighted consistency regularization

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Supervised contrastive learning for graph representation enhancement

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Knowledge based attribute completion for heterogeneous graph node classification

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Question fuzzy-attention embedding Graph-to-Tree network for intelligent math word problem solver

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Motif-aware curriculum learning for node classification

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107285openalex-snapshot

A Hierarchical Mixture-Of-Experts Framework for Few Labeled Node Classification

inferred← citeshttps://doi.org/10.1109/icwapr66259.2025.11306492openalex-snapshot

Structure-Aware Mixup for Graph Neural Networks: Enhancing Graph Representation via Structural Similarity

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Environment-Adaptive Representation Interaction for Privacy-Perturbed Graphs Against Deceptive OOD Attacks

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Hypergraph Consistency Learning With Relational Distillation

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Learning Generalizable Contrastive Representations for Graph Zero-Shot Learning

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FuzzyGSSL: An Improved Graph-based Semi-Supervised Learning Through Fuzzy Label Propagation

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Improving Pseudo-Labeling by Dynamic Confidence Calibration for Semi-Supervised Sequence Recognition

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Online Social Behavior Enhanced Detection of Political Stances in Tweets

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M-Rgnn: A Robust Gnn Based on M-Estimators for Semi-Supervised Node Classification

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