Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

نویسندگان

چکیده

Events are happening in real world and time, which can be planned organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining is challenging because typically exhibit heterogeneous texture metadata often ambiguous. In this article, we first design novel event-based meta-schema to characterize the semantic relatedness then build an network (HIN) integrating from external knowledge base. Second, propose Pairwise Popularity Graph Convolutional Network, named PP-GCN, based on weighted meta-path instance similarity textual representation inputs, perform fine-grained event categorization learn optimal weights meta-paths tasks. Third, streaming detection evolution discovery framework HINs search, historical about meta-paths, DBSCAN clustering method. Comprehensive experiments real-world data conducted compare various algorithms. Experimental results demonstrate that our proposed outperforms other alternative techniques.

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ژورنال

عنوان ژورنال: ACM Transactions on Knowledge Discovery From Data

سال: 2021

ISSN: ['1556-472X', '1556-4681']

DOI: https://doi.org/10.1145/3447585