ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks
Summary: ALG decouples GCNs to tailor active learning for graphs, balancing representativeness and informativeness. ERF-based node selection accounts for importance and correlation; NP-hardness proven with a provable-approximation algorithm; gains on four datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
BibTeX Citation
@inproceedings{zhang_sigmod21,
title = {{ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks}},
author = {Zhang, Wentao and Shen, Yu and Li, Yang and Chen, Lei and Yang, Zhi and Cui, Bin},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457325},
url = {https://dl.acm.org/doi/10.1145/3448016.3457325},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,899 | Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization | 2021 | VLDB | 6.9358695e-05 |
| 4,826 | Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses | 2024 | VLDB | 6.391978e-05 |
| 5,899 | Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines | 2023 | VLDB | 5.9545431e-05 |
| 6,526 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD | 5.7559739e-05 |
| 9,785 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 5.1283279e-05 |
| 11,458 | Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph | 2025 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,674 | Reliable Data Distillation on Graph Convolutional Network | 2020 | SIGMOD | 5.7125691e-05 |
Previous
Page 1 / 1
Next