A Capsule Network-based Model for Learning Node Embeddings
Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
In this paper, we focus on learning low-dimensional embeddings for nodes in
graph-structured data. To achieve this, we propose Caps2NE -- a new
unsupervised embedding model leveraging a network of two capsule layers.
Caps2NE induces a routing process to aggregate feature vectors of context
neighbors of a given target node at the first capsule layer, then feed these
features into the second capsule layer to infer a plausible embedding for the
target node. Experimental results show that our proposed Caps2NE obtains
state-of-the-art performances on benchmark datasets for the node classification
task. Our code is available at: \url{this https URL}.