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)

Paper ID: hd6a433b3c6aff798
Venue: SIGMOD
Year: 2020
Pagerank: 5.7125691e-05
Overall Rank: 6,674 | 55.13%
DOI: 10.1145/3318464.3389706

Incoming Non-self Citations Over Time

Authors

1. Wentao Zhang (Peking University)
2. Xupeng Miao (Peking University)
3. Yingxia Shao (Beijing Institute of Technology)
4. Jiawei Jiang (ETH Zurich)
5. Lei Chen (Hong Kong University of Science and Technology)
6. Olivier Ruas (Peking University)
7. Bin Cui (Peking University)

BibTeX Citation

@inproceedings{zhang_sigmod20,
        title = {{Reliable Data Distillation on Graph Convolutional Network}},
        author = {Zhang, Wentao and Miao, Xupeng and Shao, Yingxia and Jiang, Jiawei and Chen, Lei and Ruas, Olivier and Cui, Bin},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389706},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389706},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,134	SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00011893521
3,899	Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization	2021	VLDB	6.9358695e-05
4,945	HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training	2022	SIGMOD	6.3435891e-05
4,993	DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks	2023	SIGMOD	6.32357e-05
6,324	EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs	2023	SIGMOD	5.8137796e-05
6,463	ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks	2021	SIGMOD	5.7764694e-05
6,526	CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression	2023	SIGMOD	5.7559739e-05
8,077	Billion-Scale Bipartite Graph Embedding: A Global-Local Induced Approach	2024	VLDB	5.3942942e-05
8,603	Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization	2023	SIGMOD	5.3049212e-05

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
22	Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud	2012	VLDB	0.00055962491
211	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00024816965
3,390	Experimental Analysis of Streaming Algorithms for Graph Partitioning	2019	SIGMOD	7.3509644e-05
5,723	An Experimental Evaluation of Large Scale GBDT Systems	2019	VLDB	6.0178515e-05
7,772	A Graph Database for a Virtualized Network Infrastructure	2018	SIGMOD	5.4553959e-05
9,846	DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions	2018	SIGMOD	5.121318e-05

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