{"type":"ScholarlyArticle","title":"LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection (Extended Abstract)","authors":[{"name":"Wenlun Zhang","slug":"wenlun-zhang","given":"Wenlun","family":"Zhang","affiliation":""},{"name":"Enyan Dai","slug":"enyan-dai","given":"Enyan","family":"Dai","affiliation":""},{"name":"Kentaro Yoshioka","slug":"kentaro-yoshioka","given":"Kentaro","family":"Yoshioka","affiliation":""}],"abstract":"Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in modeling data with graph structures, yet recent research reveals their susceptibility to adversarial attacks. Traditional attack methodologies, which rely on manipulating the original graph or adding links to artificially created nodes, often prove impractical in real-world settings. This paper introduces a novel adversarial scenario involving the injection of an isolated subgraph to deceive both the link recommender and the node classifier within a GNN system. Specifically, the link recommender is mislead to propose links between targeted victim nodes and the subgraph, encouraging users to unintentionally establish connections and that would degrade the node classification accuracy, thereby facilitating a successful attack. To address this, we present the LiSA framework, which employs a dual surrogate model and bi-level optimization to simultaneously meet two adversarial objectives. Extensive experiments on real-world datasets demonstrate the effectiveness of our method.","doi":"10.24963/ijcai.2026/931","url":"https://ijcai.org/proceedings/2026/931","pdf":"https://ijcai.org/proceedings/2026/0931.pdf","markdown":"https://ijcai.org/proceedings/2026/931.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/931","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":931,"pages":{"first":8306,"last":8310},"keywords":["Data Mining: Privacy-preserving data mining","Data Mining: Recommender systems","Machine Learning: Adversarial machine learning","AI Ethics, Trust, Fairnes: Safety and robustness"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}