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Existing literature, such as feature transformation and feature selection, is labor-intensive (e.g., heavy reliance on empirical experience) and mostly designed for tabular data. Moreover, these methods regard data samples as independent, which ignores the unique topological structure when applied to graph data, thus resulting in a suboptimal reconstruction feature space. Can we consider the topological information to automatically reconstruct feature space for graph data without heavy experiential knowledge? To fill this gap, we leverage topology-aware reinforcement learning to automate and optimize feature space reconstruction for graph data. Our approach combines the extraction of core subgraphs to capture essential structural information with a graph neural network to encode topological features and reduce computing complexity. Then we introduce three reinforcement agents within a hierarchical structure to systematically generate meaningful features through an iterative process, effectively reconstructing the feature space. This framework provides a principled solution for attributed graph feature space reconstruction. The extensive experiments demonstrate the effectiveness and efficiency of including topological awareness on three widely used downstream tasks (node classification, link prediction, and graph classification). Our code and data are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/tinyurl.com\/graphFT123\">https:\/\/tinyurl.com\/graphFT123<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3774423","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T13:02:03Z","timestamp":1762261323000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Topology-aware Reinforcement Feature Space Reconstruction for Graph Data"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6196-0287","authenticated-orcid":false,"given":"Wangyang","family":"Ying","sequence":"first","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1328-9230","authenticated-orcid":false,"given":"Haoyue","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-5977","authenticated-orcid":false,"given":"Kunpeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computing, Clemson University, Clemson, South Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-8024","authenticated-orcid":false,"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Fabian Pedregosa Ga\u00ebl Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron Weiss Vincent Dubourg et al. 2011. 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