Citation notice #6715 · 2026-07-11 03:19:17.190701+00:00

Watermarking Graph Neural Networks via Explanations for Ownership Protection

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cites Graph Neural Networks: A Review of Methods and Applications,, which carries a correction notice dated 2024-01-09. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.

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doi: https://doi.org/10.1016/j.aiopen.2021.01.001. URL https://www.sciencedirect.com/science/ar ticle/pii/S2666651021000012. Zhou, Y ., Huo, H., Hou, Z., and Bu, F. A deep graph convo- lutional neural network architecture for graph classification. PLOS ONE, 18, 2023. URL https://api.semantic scholar.org/CorpusID:257428249. Zügner, D., Borchert, O., Akbarnejad, A., and Günnemann, S. Adversarial attacks on graph neural networks: Perturbations and their patterns. ACM Trans. Knowl. Discov. Data , 14(5), jun 2020. ISSN 1556-4681. URL https://doi.org/10 .1145/3394520. 11 Watermarking Graph Neural Networks via Explanations for Ownership Protection A. Appendix Algorithm 1: Watermark Embedding Input: Graph 𝐺, training nodes V𝑡𝑟 , learning rate 𝜂, #watermarked subgraphs 𝑇, watermarked subgraph size 𝑠, hyperparameter 𝑟, target significance 𝛼𝑡𝑔𝑡 , watermark loss contribution bound 𝜖. Output: A trained and watermarked model, 𝑓 . Setup: Initialize 𝑓 and optimizer. With 𝛼𝑡𝑔𝑡 , 𝑇, and number of node features 𝐹 as input, compute 𝑀 using equation 11. Initialize w with values 1 and −1 uniform at random. With 𝑛𝑠𝑢𝑏 = 𝑐𝑒𝑖𝑙 (𝑠 × |V 𝑡𝑟 |), randomly sample 𝑇 sets of 𝑛𝑠𝑢𝑏 nodes from V𝑡𝑟 . These subgraphs jo

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doi: https://doi.org/10.1016/j.aiopen.2021.01.001. URL https://www.sciencedirect.com/science/ar ticle/pii/S2666651021000012. Zhou, Y ., Huo, H., Hou, Z., and Bu, F. A deep graph convo- lutional neural network architecture for graph classification. PLOS ONE, 18, 2023. URL https://api.semantic scholar.org/CorpusID:257428249. Zügner, D., Borchert, O., Akbarnejad, A., and Günnemann, S. Adversarial attacks on graph neural networks: Perturbations and their patterns. ACM Trans. Knowl. Discov. Data, 14(5), jun 2020. ISSN 1556-4681. URL https://doi.org/10 .1145/3394520. 11 Watermarking Graph Neural Networks via Explanations for Ownership Protection A. Appendix Algorithm 1: Watermark Embedding Input: Graph 𝐺, training nodes V𝑡𝑟, learning rate 𝜂, #watermarked subgraphs 𝑇, watermarked subgraph size 𝑠, hyperparameter 𝑟, target significance 𝛼𝑡𝑔𝑡, watermark loss contribution bound 𝜖. Output: A trained and watermarked model, 𝑓 . Setup: Initialize 𝑓 and optimizer. With 𝛼𝑡𝑔𝑡, 𝑇, and number of node features 𝐹 as input, compute 𝑀 using equation 11. Initialize w with values 1 and −1 uniform at random. With 𝑛𝑠𝑢𝑏 = 𝑐𝑒𝑖𝑙 (𝑠 × |V 𝑡𝑟 |), randomly sample 𝑇 sets of 𝑛𝑠𝑢𝑏 nodes from V𝑡𝑟 . These subgraphs jo

02Event

Type: Correction
Source: Crossref
Original DOI: 10.1016/j.aiopen.2021.01.001
Notice DOI: 10.1016/j.aiopen.2024.01.002
Date: 2024-01-09
Title: Erratum regarding Declaration of Competing Interest statements in previously published articles
Reasons: ['Erratum']
Work: Graph Neural Networks: A Review of Methods and Applications, (2020) AI Open

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