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
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Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,165 | Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization | 2021 | VLDB | 6.3921956e-05 |
| 5,561 | Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses | 2024 | VLDB | 5.4332062e-05 |
| 6,884 | Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines | 2023 | VLDB | 4.8955332e-05 |
| 6,985 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD | 4.8729387e-05 |
| 9,460 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD | 4.3366491e-05 |
| 10,885 | Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph | 2025 | VLDB | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 6,566 | Reliable Data Distillation on Graph Convolutional Network | 2020 | SIGMOD | 5.0074274e-05 |
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