Semi-Supervised Graph Attention Networks for Event Representation
Learning
- URL: http://arxiv.org/abs/2201.00363v1
- Date: Sun, 2 Jan 2022 14:38:28 GMT
- Title: Semi-Supervised Graph Attention Networks for Event Representation
Learning
- Authors: Joao Pedro Rodrigues Mattos and Ricardo M. Marcacini
- Abstract summary: This paper presents GNEE (GAT Neural Event Embeddings), a method that combines Graph Attention Networks and Graph Regularization.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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