{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:15:52Z","timestamp":1766578552381,"version":"3.48.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Natural Science Foundation of Zhejiang Province, China","award":["LY22F020001"],"award-info":[{"award-number":["LY22F020001"]}]},{"name":"Natural Science Foundation of Zhejiang Province, China","award":["LZ20F020001"],"award-info":[{"award-number":["LZ20F020001"]}]},{"name":"3315 Plan Foundation of Ningbo","award":["2019B-18-G"],"award-info":[{"award-number":["2019B-18-G"]}]},{"DOI":"10.13039\/501100001809","name":"China Natural Science Foundation","doi-asserted-by":"crossref","award":["62271274"],"award-info":[{"award-number":["62271274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Application of A Multi Billion Parameter Monitoring Video Model","award":["20242004"],"award-info":[{"award-number":["20242004"]}]},{"name":"Ningbo \u201cKechuang Yongjiang 2035\u201d Key Technology","award":["2024Z245"],"award-info":[{"award-number":["2024Z245"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Graph node classification is an important field in network representation learning algorithms. With the development of deep learning, the focus has shifted to models like graph convolutional network (GCN) and graph attention network (GAT). GCN and GAT only consider direct adjacent nodes in node embeddings, so the receptive field of node embeddings is constricted which cannot fully express the network structure. To expand the receptive fields of node embeddings and strengthen the effect of graph structure on node embedding, the dual graph embedding (DGE) framework is proposed. In this framework, an attention mechanism is adopted to transform the original graph into a weighted graph, and a dual graph transform strategy is proposed to convert the weighted graph into a dual graph. The edges in the original graph indicate the nodes in the dual graph. Performing node embedding methods such as GAT\/GCN in the dual graph is performing edge embeddings in the original graph. Node embeddings in the original graph can be realized by aggregating the edge embeddings for each node. According to the DGE framework and multi-head model, we designed DGE-GCN, DGE-GCN-GAT, DGE-GCN-MULTI, and DGE-GAT-MULTI methods. Experimental results show that DGE methods outperform various node classification methods, especially in graphs with high link density.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf086","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T07:58:36Z","timestamp":1750319916000},"page":"1949-1956","source":"Crossref","is-referenced-by-count":0,"title":["A dual graph embedding based node classification method"],"prefix":"10.1093","volume":"68","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science , Ningbo University, 818 Fenghua Road, Jiangbei District, 315211 Ningbo,","place":["China"]}]},{"given":"Yefang","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science , Ningbo University, 818 Fenghua Road, Jiangbei District, 315211 Ningbo,","place":["China"]}]},{"given":"Yu","family":"Xin","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science , Ningbo University, 818 Fenghua Road, Jiangbei District, 315211 Ningbo,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"2025122407091756100_ref1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1145\/3308558.3313488","article-title":"Graph neural networks for social recommendation","volume-title":"The World Wide Web Conference, San Francisco, CA, 13\u201317 May","author":"Fan","year":"2019"},{"key":"2025122407091756100_ref2","first-page":"1049","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume-title":"Advances in Neural Information Processing Systems 31 (NIPS 2018), Montreal, Canada, 2\u20138 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