Semi-Supervised Graph Attention Networks for Event Representation Learning. (arXiv:2201.00363v1 [cs.LG])

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 …

arxiv attention event graph learning networks