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arXiv · 1911.04822

A Capsule Network-based Model for Learning Node Embeddings

Dai Quoc Nguyen·Tu Dinh Nguyen·Dat Quoc Nguyen·Dinh Phung
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Abstract

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{https://github.com/daiquocnguyen/Caps2NE}.

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Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung. 2019-11-12. A Capsule Network-based Model for Learning Node Embeddings. https://arxiv.org/abs/1911.04822

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