{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:37:00Z","timestamp":1723016220000},"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":[[2018,7]]},"abstract":"<jats:p>Combinational\u00a0 network embedding, which learns the node representation by exploring both\u00a0 topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other.\u00a0\u00a0Most of the existing methods either consider the\u00a0 topological and non-topological\u00a0 information being aligned or possess predetermined preferences during the embedding process.Unfortunately, previous methods\u00a0 fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts.\u00a0To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework.\u00a0The proposed model automatically leans to the information which is more discriminative.The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/502","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"3613-3619","source":"Crossref","is-referenced-by-count":1,"title":["3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces"],"prefix":"10.24963","author":[{"given":"Liang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hebei University of Technology"},{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, CAS"}]},{"given":"Yuanfang","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, CAS"}]},{"given":"Di","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"}]},{"given":"Xiaochun","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, CAS"}]}],"member":"10584","event":{"number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2018","name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","start":{"date-parts":[[2018,7,13]]},"theme":"Artificial Intelligence","location":"Stockholm, Sweden","end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:53:27Z","timestamp":1530755607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/502"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/502","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}