{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:50:56Z","timestamp":1775278256067,"version":"3.50.1"},"reference-count":15,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2026,4,1]]},"DOI":"10.1587\/transinf.2025edl8040","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T22:08:53Z","timestamp":1760566133000},"page":"556-559","source":"Crossref","is-referenced-by-count":0,"title":["GRFA: Leveraging Adaptive Graph Reconstruction for Fake Account Detection in Social Networks"],"prefix":"10.1587","volume":"E109.D","author":[{"given":"Shibo","family":"ZHANG","sequence":"first","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchang","family":"CHEN","sequence":"additional","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxin","family":"LIU","sequence":"additional","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"LI","sequence":"additional","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingle","family":"LI","sequence":"additional","affiliation":[{"name":"Information Engineering University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Gao, X. Wang, X. He, H. Feng, and Y. Zhang, \u201cRumor detection with self-supervised learning on texts and social graph,\u201d Frontiers of Computer Science, vol.17, no.4, p.174611, 2023. 10.1007\/s11704-022-1531-9","DOI":"10.1007\/s11704-022-1531-9"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] X. Zeng, H. Peng, and A. Li, \u201cAdversarial socialbots modeling based on structural information principles,\u201d Proceedings of the AAAI Conference on Artificial Intelligence, vol.38, no.1, pp.392-400, 2024. 10.1609\/aaai.v38i1.27793","DOI":"10.1609\/aaai.v38i1.27793"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] S. Feng, H. Wan, N. Wang, and M. Luo, \u201cBotrgcn: Twitter bot detection with relational graph convolutional networks,\u201d Proceedings of the 2021 IEEE\/ACM international conference on advances in social networks analysis and mining, pp.236-239, 2021. 10.1145\/3487351.3488336","DOI":"10.1145\/3487351.3488336"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Feng, Z. Tan, R. Li, and M. Luo, \u201cHeterogeneity-aware twitter bot detection with relational graph transformers,\u201d Proceedings of the AAAI Conference on Artificial Intelligence, vol.36, no.4, pp.3977-3985, 2022. 10.1609\/aaai.v36i4.20314","DOI":"10.1609\/aaai.v36i4.20314"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Yang, Q. Wu, B. He, H. Peng, R. Yang, Z. Hao, and Y. Liao, \u201cSebot: Structural entropy guided multi-view contrastive learning for social bot detection,\u201d Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.3841-3852, 2024.","DOI":"10.1145\/3637528.3671871"},{"key":"6","unstructured":"[6] M. Zhou and D. Zhang, \u201cLgb: Language model and graph neural network-driven social bot detection (2024). doi: 10.48550,\u201d arXiv preprint arXiv.2406.08762, 2024."},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] Q. Lv, M. Ding, Q. Liu, Y. Chen, W. Feng, S. He, C. Zhou, J. Jiang, Y. Dong, and J. Tang, \u201cAre we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks,\u201d Proceedings of the 27th ACM SIGKDD conference, pp.1150-1160, 2021. 10.1145\/3447548.3467350","DOI":"10.1145\/3447548.3467350"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, \u201cFame for sale: Efficient detection of fake twitter followers,\u201d Decision Support Systems, vol.80, pp.56-71, 2015. 10.1016\/j.dss.2015.09.003","DOI":"10.1016\/j.dss.2015.09.003"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] S. Feng, H. Wan, N. Wang, J. Li, and M. Luo, \u201cTwibot-20: A comprehensive twitter bot detection benchmark,\u201d Proceedings of the 30th ACM international conference on information & knowledge management, pp.4485-4494, 2021. 10.1145\/3459637.3482019","DOI":"10.1145\/3459637.3482019"},{"key":"10","unstructured":"[10] S. Shi, K. Qiao, J. Chen, S. Yang, J. Yang, B. Song, L. Wang, and B. Yan, \u201cMgtab: A multi-relational graph-based twitter account detection benchmark,\u201d arXiv preprint arXiv:2301.01123, 2023."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] A. Sha, B. Wang, X. Wu, L. Zhang, B. Hu, and J.Q. Zhang, \u201cSemi-supervised classification for hyperspectral images using edge-conditioned graph convolutional networks,\u201d IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, pp.2690-2693, IEEE, 2019. 10.1109\/igarss.2019.8898688","DOI":"10.1109\/IGARSS.2019.8898688"},{"key":"12","unstructured":"[12] P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, et al., \u201cGraph attention networks,\u201d Proceedings of the International Conference on Learning Representations, Washington, USA, ICLR, 2018."},{"key":"13","unstructured":"[13] W. Hamilton, Z. Ying, and J. Leskovec, \u201cInductive representation learning on large graphs,\u201d Advances in neural information processing systems, vol.30, 2017."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Luan, C. Hua, Q. Lu, J. Zhu, M. Zhao, S. Zhang, X.W. Chang, and D. Precup, \u201cRevisiting heterophily for graph neural networks,\u201d Advances in neural information processing systems, vol.35, pp.1362-1375, 2022.","DOI":"10.52202\/068431-0100"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] X. Lou, G. Liu, and J. Li, \u201cHeterogeneous graph neural network with graph-data augmentation and adaptive denoising,\u201d Applied Intelligence, vol.54, no.5, pp.4411-4424, 2024.","DOI":"10.1007\/s10489-024-05363-8"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E109.D\/4\/E109.D_2025EDL8040\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:57:11Z","timestamp":1775275031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E109.D\/4\/E109.D_2025EDL8040\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":15,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2025edl8040","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]},"article-number":"2025EDL8040"}}