{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:14:53Z","timestamp":1760238893043,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hubei Province","award":["2021CFB139","2662020XXQD002"],"award-info":[{"award-number":["2021CFB139","2662020XXQD002"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2021CFB139","2662020XXQD002"],"award-info":[{"award-number":["2021CFB139","2662020XXQD002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous works typically focus on using the graph pooling scheme in a linear manner. In this paper, we propose the real quadratic-form-based graph pooling framework for graph neural networks in graph classification. The quadratic form can capture a pairwise relationship, which brings a stronger expressive power than existing linear forms. Experiments on benchmarks verify the effectiveness of the proposed graph pooling scheme based on the quadratic form in graph classification tasks.<\/jats:p>","DOI":"10.3390\/make4030027","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T04:12:01Z","timestamp":1655871121000},"page":"580-590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3540-5775","authenticated-orcid":false,"given":"Youfa","family":"Liu","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, Wuhan 430071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-2722","authenticated-orcid":false,"given":"Guo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Business, Hubei University, Wuhan 430062, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"ref_1","unstructured":"Kipf, T.N., and Welling, M. 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