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They assume that the attack patterns have been known, e.g., attackers tend to add edges between dissimilar nodes. Then, they remove edges between dissimilar nodes from attacked graphs, aiming to alleviate the negative impact of adversarial attacks. Nevertheless, on dynamic graphs, attackers can change their attack strategies at different times, making existing passive GNN defenders that are passively designed for specific attack patterns fail to resist attacks. In this paper, we propose a novel active GNN defender for dynamic graphs, namely ADGNN, which actively injects guardian nodes to protect target nodes from effective attacks. Specifically, we first formulate an active defense objective to design guardian node behaviors. This objective targets to disrupt the prediction of attackers and protect easily attacked nodes, thereby preventing attackers from generating effective attacks. Then, we propose a gradient-based algorithm with two acceleration techniques to optimize this objective. Extensive experiments on four real-world graph datasets demonstrate the effectiveness of our proposed defender and its capacity to enhance existing GNN defenders.<\/jats:p>","DOI":"10.14778\/3659437.3659457","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T12:22:27Z","timestamp":1717158147000},"page":"2050-2063","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense"],"prefix":"10.14778","volume":"17","author":[{"given":"Haoyang","family":"Li","sequence":"first","affiliation":[{"name":"HKUST"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shimin","family":"Di","sequence":"additional","affiliation":[{"name":"HKUST"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Calvin Hong Yi","family":"Li","sequence":"additional","affiliation":[{"name":"The Cigna Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"HKUST(GZ)&amp;HKUST"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Zhou","sequence":"additional","affiliation":[{"name":"HKUST"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon. 2024. amazon.com.au. https:\/\/www.amazon.com.au\/. 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