Back to papers
Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense
Summary: ADGNN: active defense for dynamic graphs that injects optimizable guardian nodes to disrupt attackers' node-injection strategies. Optimizes an active-defense objective via a gradient-based algorithm with two speedups, outperforming passive pattern-specific defenders and augmenting existing GNN defenses.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13438
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.4952414e-05
- Overall Rank
- 8,510 | 40.80%
- DOI
-
10.14778/3659437.3659457
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 636 |
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding |
2021 |
SIGMOD |
0.00018846494 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 2,897 |
ICS-GNN: Lightweight Interactive Community Search via Graph Neural Network |
2021 |
VLDB |
7.9450406e-05 |
| 3,369 |
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed |
2022 |
VLDB |
7.171452e-05 |
| 3,709 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8242482e-05 |
| 3,803 |
Scaling Attributed Network Embedding to Massive Graphs |
2021 |
VLDB |
6.7550628e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5611714e-05 |
| 4,047 |
Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees |
2023 |
SIGMOD |
6.4972105e-05 |
| 4,049 |
Building Enclave-Native Storage Engines for Practical Encrypted Databases |
2021 |
VLDB |
6.4955754e-05 |
| 4,165 |
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization |
2021 |
VLDB |
6.3921956e-05 |
| 4,206 |
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting |
2022 |
VLDB |
6.3595566e-05 |
| 5,420 |
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization |
2022 |
VLDB |
5.5157743e-05 |
| 5,443 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.5025808e-05 |
| 5,475 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4869706e-05 |
| 5,491 |
R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys |
2022 |
SIGMOD |
5.4776364e-05 |
| 5,521 |
Efficient Streaming Subgraph Isomorphism with Graph Neural Networks |
2021 |
VLDB |
5.4614637e-05 |
| 5,895 |
METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting |
2022 |
VLDB |
5.2857247e-05 |
| 6,485 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0453531e-05 |
| 6,718 |
Operon: An Encrypted Database for Ownership-Preserving Data Management |
2022 |
VLDB |
4.9505599e-05 |
| 7,064 |
Residual Sensitivity for Differentially Private Multi-Way Joins |
2021 |
SIGMOD |
4.8450749e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 7,579 |
A Nearly Instance-optimal Differentially Private Mechanism for Conjunctive Queries |
2022 |
PODS |
4.706055e-05 |
| 9,272 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
4.3652496e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.319218e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 7,566 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7089968e-05 |
| 6,625 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
4.9889819e-05 |
| 10,735 |
Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation |
2025 |
VLDB |
4.1945683e-05 |
| 6,153 |
On Data-Aware Global Explainability of Graph Neural Networks |
2023 |
VLDB |
5.1829258e-05 |
| 3,369 |
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed |
2022 |
VLDB |
7.171452e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 6,942 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8922884e-05 |
| 6,485 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0453531e-05 |
| 5,443 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.5025808e-05 |