{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T17:58:13Z","timestamp":1777053493628,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Multi\u2010view learning has emerged as a pivotal research area driven by the growing heterogeneity of real\u2010world data, and graph neural network-based models, modeling multi-view data as multi-view graphs, have achieved remarkable performance by revealing its deep semantics.  However, by assuming cross\u2010view consistency, most approaches collect not only task-relevant (determinative) semantics but also symbiotic yet task-irrelevant (incidental) factors are collected to obscure model inference. Furthermore, these approaches often lack rigorous theoretical analysis that bridges training data to test data. To address these issues, we propose Target-oriented Graph Neural Network (TGNN), a novel framework that goes beyond traditional consistency by prioritizing task-relevant information, ensuring alignment with the target. Specifically, TGNN employs a class-level dual-objective loss to minimize the classification similarity between determinative and incidental factors, accentuating the former while suppressing the latter during model inference. Meanwhile, to ensure consistency between the learned semantics and predictions in representation learning, we introduce a penalty term that aims to amplify the divergence between these two types of factors. Furthermore, we derive an upper bound on the loss discrepancy between training and test data, providing formal guarantees for generalization to test domains. Extensive experiments conducted on three types of multi-view datasets validate the superiority of TGNN.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/604","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"5426-5434","source":"Crossref","is-referenced-by-count":1,"title":["Going Beyond Consistency: Target-oriented Multi-view Graph Neural Network"],"prefix":"10.24963","author":[{"given":"Sujia","family":"Huang","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology"}]},{"given":"Lele","family":"Fu","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University"}]},{"given":"Shuman","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Fuzhou University"}]},{"given":"Yide","family":"Qiu","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}]},{"given":"Zhen","family":"Cui","sequence":"additional","affiliation":[{"name":"Beijing Normal University"}]},{"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:34:35Z","timestamp":1758627275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/604"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/604","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}