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A\nthorough evaluation of these models is performed, and comparisons are made\nagainst established benchmarks. To provide a meaningful comparison, we retrain\nRelational Graph Convolutional Networks, the spectral counterpart of Relational\nGraph Attention Networks, and evaluate them under the same conditions. We find\nthat Relational Graph Attention Networks perform worse than anticipated,\nalthough some configurations are marginally beneficial for modelling molecular\nproperties. We provide insights as to why this may be, and suggest both\nmodifications to evaluation strategies, as well as directions to investigate\nfor future work.","url_abs":"http://arxiv.org/abs/1904.05811v1","url_pdf":"http://arxiv.org/pdf/1904.05811v1.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":[{"paper_slug":"relational-graph-attention-networks-1","repo_url":"https://github.com/Babylonpartners/rgat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"relational-graph-attention-networks-1","repo_url":"https://github.com/markWJJ/rgat","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"graph-attention","task_name":"Graph Attention"}],"methods":[{"method_slug":"graph-convolutional-networks","method_name":"Graph Convolutional Networks"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.05811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05811"}},"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. 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