{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/relaxing-graph-transformers-for-adversarial","title":"Relaxing Graph Transformers for Adversarial Attacks","arxiv_id":"2407.11764","date":"2024-07-16","proceeding":null,"authors":["Philipp Foth","Lukas Gosch","Simon Geisler","Leo Schwinn","Stephan Günnemann"],"abstract":"Existing studies have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks. Even though Graph Transformers (GTs) surpassed Message-Passing GNNs on several benchmarks, their adversarial robustness properties are unexplored. However, attacking GTs is challenging due to their Positional Encodings (PEs) and special attention mechanisms which can be difficult to differentiate. We overcome these challenges by targeting three representative architectures based on (1) random-walk PEs, (2) pair-wise-shortest-path PEs, and (3) spectral PEs - and propose the first adaptive attacks for GTs. We leverage our attacks to evaluate robustness to (a) structure perturbations on node classification; and (b) node injection attacks for (fake-news) graph classification. Our evaluation reveals that they can be catastrophically fragile and underlines our work's importance and the necessity for adaptive attacks.","url_abs":"https://arxiv.org/abs/2407.11764v1","url_pdf":"https://arxiv.org/pdf/2407.11764v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"gts","method_name":"GTS"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.11764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11764"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/isefos/gt_robustness","reach":null}],"summary":{"ran_fixture":1},"by_repo_kind":{"found_in_text":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"ddce039edac7cc0d","entry":"apply_node_mask","repo":"isefos/gt_robustness","repo_kind":"found_in_text","path":"graphgps/attack/attack.py","file_url":"https://github.com/isefos/gt_robustness/blob/HEAD/graphgps/attack/attack.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ddce039edac7cc0d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}