{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:31:16Z","timestamp":1783787476971,"version":"3.55.0"},"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":[[2021,8]]},"abstract":"<jats:p>Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with graph data. To be specific, MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. The feature entropy, which assumes the embeddings of adjacent nodes to be more similar, connects node features and link topology on graphs. The structure entropy takes the normalized degree as basic unit to further measure the higher-order structure of graphs. Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph. Finally, comprehensive experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/381","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"2767-2774","source":"Crossref","is-referenced-by-count":25,"title":["Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks"],"prefix":"10.24963","author":[{"given":"Gongxu","family":"Luo","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"},{"name":"School of Computer Science and Engineering, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lehigh University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifang","family":"He","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lehigh University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:02:58Z","timestamp":1628679778000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/381"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/381","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}