{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:15:51Z","timestamp":1778152551086,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,25]],"date-time":"2022-12-25T00:00:00Z","timestamp":1671926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provinces Key Research and Development Plan Information Secure Sharing and Privacy Protection Technology for IoT Project","award":["2020YFG0292"],"award-info":[{"award-number":["2020YFG0292"]}]},{"name":"Sichuan Provinces Key Research and Development Plan Information Secure Sharing and Privacy Protection Technology for IoT Project","award":["6142103190207"],"award-info":[{"award-number":["6142103190207"]}]},{"name":"Science and Technology on Communication Security Laboratory of China","award":["2020YFG0292"],"award-info":[{"award-number":["2020YFG0292"]}]},{"name":"Science and Technology on Communication Security Laboratory of China","award":["6142103190207"],"award-info":[{"award-number":["6142103190207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.<\/jats:p>","DOI":"10.3390\/e25010038","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T04:42:47Z","timestamp":1672116167000},"page":"38","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhanced Signed Graph Neural Network with Node Polarity"],"prefix":"10.3390","volume":"25","author":[{"given":"Jiawang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7860-5208","authenticated-orcid":false,"given":"Zhenqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shaanxi Normal University, Xi\u2019an 710062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1109\/TKDE.2020.3008732","article-title":"A graph neural network framework for social recommendations","volume":"34","author":"Fan","year":"2020","journal-title":"IEEE Trans. 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