{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:58:27Z","timestamp":1773511107789,"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":[[2023,8]]},"abstract":"<jats:p>This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA). It explicitly passes the information among nodes and edges in three ways, i.e. node-to-node, node-to-edge, and edge-to-node, which is essential for learning graph-structured data. On this basis, we design an effective transformer architecture named Graph Propagation Transformer (GPTrans) to further help learn graph data. We verify the performance of GPTrans in a wide range of graph learning experiments on several benchmark datasets. These results show that our method outperforms many state-of-the-art transformer-based graph models with better performance. The code will be released at https:\/\/github.com\/czczup\/GPTrans.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/396","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"3559-3567","source":"Crossref","is-referenced-by-count":13,"title":["Graph Propagation Transformer for Graph Representation Learning"],"prefix":"10.24963","author":[{"given":"Zhe","family":"Chen","sequence":"first","affiliation":[{"name":"Nanjing University"}]},{"given":"Hao","family":"Tan","sequence":"additional","affiliation":[{"name":"OPPO Research Institute"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Tianrun","family":"Shen","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Tong","family":"Lu","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Qiuying","family":"Peng","sequence":"additional","affiliation":[{"name":"OPPO Research Institute"}]},{"given":"Cheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"OPPO Research Institute"}]},{"given":"Yue","family":"Qi","sequence":"additional","affiliation":[{"name":"OPPO Research Institute"}]}],"member":"10584","event":{"name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","theme":"Artificial Intelligence","location":"Macau, SAR China","acronym":"IJCAI-2023","number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2023,8,19]]},"end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:47:46Z","timestamp":1691743666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/396"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/396","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}