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Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization

Summary: Scapin reframes perturbation for GNNs as influence-maximization, enabling scalable edge edits. Proposes edge-influence model with decomposed, submodular objectives and a submodularity-based edge addition algorithm, yielding runtime/memory efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6711
Venue
SIGMOD
Year
2023
Pagerank
5.2993405e-05
Overall Rank
9,243 | 36.59%
DOI
10.1145/3589291

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod23,
        title = {{Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization}},
        author = {Wang, Yexin and Yang, Zhi and Liu, Junqi and Zhang, Wentao and Cui, Bin},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589291},
        url = {https://dl.acm.org/doi/10.1145/3589291},
        year = {2023}
}

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