{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:02:32Z","timestamp":1772121752542,"version":"3.50.1"},"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>This paper presents HGEN that pioneers ensemble learning for heterogeneous graphs. We argue that the heterogeneity in node types, nodal features, and local neighborhood topology poses significant challenges for ensemble learning, particularly in accommodating diverse graph learners. Our HGEN framework ensembles multiple learners through a meta-path and transformation-based optimization pipeline to uplift classification accuracy. Specifically, HGEN uses meta-path combined with random dropping to create Allele Graph Neural Networks (GNNs), whereby the base graph learners are trained and aligned for later ensembling. To ensure effective ensemble learning, HGEN presents two key components:1) a residual-attention mechanism to calibrate allele GNNs of different meta-paths, thereby enforcing node embeddings to focus on more informative graphs to improve base learner accuracy, and 2) a correlation-regularization term to enlarge the disparity among embedding matrices generated from different meta-paths, thereby enriching base learner diversity. We analyze the convergence of  HGEN and attest its higher regularization magnitude over simple voting. Experiments on five heterogeneous networks validate that HGEN consistently outperforms its state-of-the-art competitors by substantial margin. Codes are available at https:\/\/github.com\/Chrisshen12\/HGEN.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/685","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"6156-6163","source":"Crossref","is-referenced-by-count":1,"title":["HGEN: Heterogeneous Graph Ensemble Networks"],"prefix":"10.24963","author":[{"given":"Jiajun","family":"Shen","sequence":"first","affiliation":[{"name":"Dept. of Electrical Engineering and Computer Science, Florida Atlantic University, USA"}]},{"given":"Yufei","family":"Jin","sequence":"additional","affiliation":[{"name":"Dept. of Electrical Engineering and Computer Science, Florida Atlantic University, USA"}]},{"given":"Kaibu","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Data Science, William & Mary, USA"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"Department of Data Science, William & Mary, USA"}]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Dept. of Electrical Engineering and Computer Science, Florida Atlantic University, USA"}]}],"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":"2025-09-23T11:34:50Z","timestamp":1758627290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/685"}},"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\/685","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}