Reliable Data Distillation on Graph Convolutional Network
Summary: Reliable Data Distillation defines node and edge reliability to leverage unlabeled data in semi-supervised GCNs. Proposes a data-reliability–driven ensemble and a Self-Boosting SSL framework to curb teacher bias and cost, yielding state-of-the-art semi-supervised node classification. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wentao Zhang
- 2. Xupeng Miao
- 3. Yingxia Shao
- 4. Jiawei Jiang
- 5. Lei Chen
- 6. Olivier Ruas
- 7. Bin Cui
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 37 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.0007522744 |
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 3,839 | Experimental Analysis of Streaming Algorithms for Graph Partitioning | 2019 | SIGMOD | 6.7120651e-05 |
| 4,975 | An Experimental Evaluation of Large Scale GBDT Systems | 2019 | VLDB | 5.79026e-05 |
| 7,143 | A Graph Database for a Virtualized Network Infrastructure | 2018 | SIGMOD | 4.8191495e-05 |
| 9,469 | DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions | 2018 | SIGMOD | 4.3342363e-05 |
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