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Revisiting Semi-Supervised Learning with Graph Embeddings

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arxiv 1603.08861 v2 pith:CHH2MTEX submitted 2016-03-29 cs.LG

Zhilin Yang , William W. Cohen , Ruslan Salakhutdinov This is my paper · ORCID
classification cs.LG
keywords embeddingsgraphclassclassificationentityfeatureinductiveinstances
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We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement

    cs.LG 2026-07 conditional novelty 6.0 of 10

    ACE adds a heterophily-aware auxiliary loss to coarsening-based GNN training, recovering discarded node-level information and improving accuracy on heterophilic graphs by up to ~15 points.

  2. Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A partition-wise graph filtering method, CPF, unifies graph-wise and node-wise filtering and achieves state-of-the-art node classification on 13 benchmark graphs and anomaly detection on 3 datasets.

  3. Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A graph-structured failure-mode dataset for autonomous cargo ships, built with Word2Vec, BERT-KPCA, and Sentence-BERT features, is presented with GNN classification accuracy of 0.735.

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