{"type":"ScholarlyArticle","title":"Tag2Gauss: Learning Tag Representations via Gaussian Distribution in Tagged Networks","authors":[{"name":"Yun Wang","slug":"yun-wang","given":"Yun","family":"Wang","affiliation":"Key Laboratory of Machine Perception (MOE) , Peking University , Beijing , China"},{"name":"Lun Du","slug":"lun-du","given":"Lun","family":"Du","affiliation":"Key Laboratory of Machine Perception (MOE) , Peking University , Beijing , China"},{"name":"Guojie Song","slug":"guojie-song","given":"Guojie","family":"Song","affiliation":"Key Laboratory of Machine Perception (MOE) , Peking University , Beijing , China"},{"name":"Xiaojun Ma","slug":"xiaojun-ma","given":"Xiaojun","family":"Ma","affiliation":"Key Laboratory of Machine Perception (MOE) , Peking University , Beijing , China"},{"name":"Lichen Jin","slug":"lichen-jin","given":"Lichen","family":"Jin","affiliation":"Key Laboratory of Machine Perception (MOE) , Peking University , Beijing , China"},{"name":"Wei Lin","slug":"wei-lin","given":"Wei","family":"Lin","affiliation":"Alibaba Group , Beijing , China"},{"name":"Fei Sun","slug":"fei-sun","given":"Fei","family":"Sun","affiliation":"Alibaba Group , Beijing , China"}],"abstract":"Keyword-based tags (referred to as tags) are used to represent additional attributes of nodes in addition to what is explicitly stated in their contents, like the hashtags in YouTube. Aside of being auxiliary information for node representation, tags can also be used for retrieval, recommendation, content organization, and event analysis. Therefore, tag representation learning is of great importance. However, to learn satisfactory tag representations is challenging because 1) traditional representation methods generally fail when it comes to representing tags, 2) bidirectional interactions between nodes and tags should be modeled, which are generally not dealt within existing research works. In this paper, we propose a tag representation learning model which takes tag-related node interaction into consideration, named Tag2Gauss. Specifically, since tags represent node communities with intricate overlapping relationships, we propose that Gaussian distributions would be appropriate in modeling tags. Considering the bidirectional interactions between nodes and tags, we propose a tag representation learning model mapping tags to distributions consisting of two embedding tasks, namely Tag-view embedding and Node-view embedding. Extensive evidence demonstrates the effectiveness of representing tag as a distribution, and the advantages of the proposed architecture in many applications, such as the node classification and the network visualization.","doi":"10.24963/ijcai.2019/527","url":"https://ijcai.org/proceedings/2019/527","pdf":"https://ijcai.org/proceedings/2019/0527.pdf","markdown":"https://ijcai.org/proceedings/2019/527.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/527","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":527,"pages":{"first":3799,"last":3805},"keywords":["Machine Learning: Data Mining","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2011] Charu C Aggarwal. 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