| Full text | |
| Author(s): |
Rodrigues Mattos, Joao Pedro
;
Marcacini, Ricardo M.
;
Bailey, J
;
Miettinen, P
;
Koh, YS
;
Tao, D
;
Wu, X
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021); v. N/A, p. 6-pg., 2021-01-01. |
| Abstract | |
Event analysis from news and social networks is very useful for a wide range of social studies and real-world applications. Recently, event graphs have been explored to model event datasets and their complex relationships, where events are vertices connected to other vertices representing locations, people's names, dates, and various other event metadata. Graph representation learning methods are promising for extracting latent features from event graphs to enable the use of different classification algorithms. However, existing methods fail to meet essential requirements for event graphs, such as (i) dealing with semi-supervised graph embedding to take advantage of some labeled events, (ii) automatically determining the importance of the relationships between event vertices and their metadata vertices, as well as (iii) dealing with the graph heterogeneity. This paper presents GNEE (GAT Neural Event Embeddings), a method that combines Graph Attention Networks and Graph Regularization. First, an event graph regularization is proposed to ensure that all graph vertices receive event features, thereby mitigating the graph heterogeneity drawback. Second, semi-supervised graph embedding with self-attention mechanism considers existing labeled events, as well as learns the importance of relationships in the event graph during the representation learning process. A statistical analysis of experimental results with five real-world event graphs and six graph embedding methods shows that our GNEE outperforms state-of-the-art semi-supervised graph embedding methods. (AU) | |
| FAPESP's process: | 19/25010-5 - Semantically enriched representations for Portuguese textmining: models and applications |
| Grantee: | Solange Oliveira Rezende |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |