Content Augmented Graph Neural Networks
Fatemeh Gholamzadeh Nasrabadi AmirHossein Kashani Pegah Zahedi Mostafa Haghir Chehreghani
In recent years, graph neural networks (GNNs) have become a popular tool for
solving various problems over graphs. In these models, the link structure of
the graph is typically exploited and nodes' embeddings are iteratively updated
based on adjacent nodes. Nodes' contents are used solely in the form of feature
vectors, served as nodes' first-layer embeddings. However, the filters or
convolutions, applied during iterations/layers to these initial embeddings lead
to their impact diminish and contribute insignificantly to the final
embeddings. In order to address this issue, in this paper we propose augmenting
nodes' embeddings by embeddings generating from their content, at higher GNN
layers. More precisely, we propose models wherein a structural embedding using
a GNN and a content embedding are computed for each node. These two are
combined using a combination layer to form the embedding of a node at a given
layer. We suggest methods such as using an auto-encoder or building a content
graph, to generate content embeddings. In the end, by conducting experiments
over several real-world datasets, we demonstrate the high accuracy and
performance of our models.