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In graph applications such as molecule and polymer property prediction, identifying representative subgraph structures named as graph rationales plays an essential role in the performance of graph neural networks. Existing graph pooling and/or distribution intervention methods suffer from lack of examples to learn to identify optimal graph rationales. In this work, we introduce a new augmentation operation called environment replacement that automatically creates virtual data examples to improve rationale identification. We propose an efficient framework that performs rationale-environment separation and representation learning on the real and augmented examples in latent spaces to avoid the high complexity of explicit graph decoding and encoding. Comparing against recent techniques, experiments on seven molecular and four polymer real datasets demonstrate the effectiveness and efficiency of the proposed augmentation-based graph rationalization framework.","url_abs":"https://arxiv.org/abs/2206.02886v2","url_pdf":"https://arxiv.org/pdf/2206.02886v2.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":"graph-rationalization-with-environment-based","repo_url":"https://github.com/liugangcode/GREA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-regression","task_name":"Graph Regression"},{"task_slug":"property-prediction","task_name":"Property Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[{"slug":"o2perm","name":"$O_2$Perm","full_name":"Oxygen Permeability"},{"slug":"glasstemp","name":"GlassTemp","full_name":"Glass Transition Temperature"},{"slug":"meltingtemp","name":"MeltingTemp","full_name":"Melting Temperature"},{"slug":"polydensity","name":"PolyDensity","full_name":"Polymer Density"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-regression-on-glasstemp","task":"Graph Regression","dataset":"GlassTemp","model":"GREA (GIN)","rank_in_archive_order":1,"of":1,"metrics":{"RMSE ":"41.2±0.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.02886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.02886"}},"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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