{"type":"ScholarlyArticle","title":"Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks","authors":[{"name":"Hao Peng","slug":"hao-peng","given":"Hao","family":"Peng","affiliation":"Beijing Advanced Innovation Center for Big Data and Brain Computing , Beihang University; State Key Laboratory of Software Development Environment , Beihang University"},{"name":"Jianxin Li","slug":"jianxin-li","given":"Jianxin","family":"Li","affiliation":"Beijing Advanced Innovation Center for Big Data and Brain Computing , Beihang University; State Key Laboratory of Software Development Environment , Beihang University"},{"name":"Qiran Gong","slug":"qiran-gong","given":"Qiran","family":"Gong","affiliation":"Beijing Advanced Innovation Center for Big Data and Brain Computing , Beihang University; State Key Laboratory of Software Development Environment , Beihang University"},{"name":"Yangqiu Song","slug":"yangqiu-song","given":"Yangqiu","family":"Song","affiliation":"Department of Computer Science and Engineering , Hong Kong University of Science and Technology"},{"name":"Yuanxin Ning","slug":"yuanxin-ning","given":"Yuanxin","family":"Ning","affiliation":"Beijing Advanced Innovation Center for Big Data and Brain Computing , Beihang University; State Key Laboratory of Software Development Environment , Beihang University"},{"name":"Kunfeng Lai","slug":"kunfeng-lai","given":"Kunfeng","family":"Lai","affiliation":"Platform and Content Group , Tencent"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Institute for Data Science , Tsinghua University; Department of Computer Science , University of Illinois at Chicago"}],"abstract":"Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques.","doi":"10.24963/ijcai.2019/449","url":"https://ijcai.org/proceedings/2019/449","pdf":"https://ijcai.org/proceedings/2019/0449.pdf","markdown":"https://ijcai.org/proceedings/2019/449.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/449","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":449,"pages":{"first":3238,"last":3245},"keywords":["Machine Learning: Data Mining","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal and Subbian, 2012] Charu C Aggarwal and Karthik Subbian. 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