{"type":"ScholarlyArticle","title":"Layer-Assisted Neural Topic Modeling over Document Networks","authors":[{"name":"Yiming Wang","slug":"yiming-wang","given":"Yiming","family":"Wang","affiliation":""},{"name":"Ximing Li","slug":"ximing-li","given":"Ximing","family":"Li","affiliation":""},{"name":"Jihong Ouyang","slug":"jihong-ouyang","given":"Jihong","family":"Ouyang","affiliation":""}],"abstract":"Neural topic modeling provides a flexible, efficient, and powerful way to extract topic representations from text documents. Unfortunately, most existing models cannot handle the text data with network links, such as web pages with hyperlinks and scientific papers with citations. To resolve this kind of data, we develop a novel neural topic model , namely Layer-Assisted Neural Topic Model (LANTM), which can be interpreted from the perspective of variational auto-encoders. Our major motivation is to enhance the topic representation encoding by not only using text contents, but also the assisted network links. Specifically, LANTM encodes the texts and network links to the topic representations by an augmented network with graph convolutional modules, and decodes them by maximizing the likelihood of the generative process. The neural variational inference is adopted for efficient inference. Experimental results validate that LANTM significantly outperforms the existing  models on topic quality, text classification and link prediction..","doi":"10.24963/ijcai.2021/433","url":"https://ijcai.org/proceedings/2021/433","pdf":"https://ijcai.org/proceedings/2021/0433.pdf","markdown":"https://ijcai.org/proceedings/2021/433.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/433","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":433,"pages":{"first":3148,"last":3154},"keywords":["Machine Learning: Learning Graphical Models","Uncertainty in AI: Graphical Models","Uncertainty in AI: Bayesian Networks"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}