Fine-grained Event Categorization with Heterogeneous Graph Convolutional
Networks
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