{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:07Z","timestamp":1758672907675,"version":"3.44.0"},"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>Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-world heterophilic graphs. Existing models for heterophilic graphs primarily rely on pairwise relationships, overlooking multi-scale information from higher-order structures. This leads to suboptimal performance, particularly under noise from conflicting class information across nodes. To address these challenges, we propose HPGNN, a novel model integrating Higher-order Personalized PageRank with Graph Neural Networks. HPGNN introduces an efficient high-order approximation of Personalized PageRank (PPR) to capture long-range and multiscale node interactions. This approach reduces computational complexity and mitigates noise from surrounding information. By embedding higher-order structural information into convolutional networks, HPGNN effectively models key interactions across diverse graph dimensions. Extensive experiments on benchmark datasets demonstrate HPGNN\u2019s effectiveness. The model achieves better performance than five out of seven state-of-the-art methods on heterophilic graphs in downstream tasks while maintaining competitive performance on homophilic graphs. HPGNN\u2019s ability to balance multi-scale information and robustness to noise makes it a versatile solution for real-world graph learning challenges. Codes are available at https:\/\/github.com\/streetcorner\/HPGNN.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/724","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"6506-6514","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Yumeng","family":"Wang","sequence":"first","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]},{"given":"Zengyi","family":"Wo","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"},{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}]},{"given":"Wenjun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"},{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"},{"name":"Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, Hainan, China"}]},{"given":"Xingcheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, China"}]},{"given":"Minglai","family":"Shao","sequence":"additional","affiliation":[{"name":"School of New Media and Communication, Tianjin University, Tianjin, China"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","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":"2025-09-23T11:34:58Z","timestamp":1758627298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/724"}},"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\/724","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}