  With the explosion of graph-structured data, link prediction has emerged as
an increasingly important task. Embedding methods for link prediction utilize
neural networks to generate node embeddings, which are subsequently employed to
predict links between nodes. However, the existing embedding methods typically
take a holistic strategy to learn node embeddings and ignore the entanglement
of latent factors. As a result, entangled embeddings fail to effectively
capture the underlying information and are vulnerable to irrelevant
information, leading to unconvincing and uninterpretable link prediction
results. To address these challenges, this paper proposes a novel framework
with two variants, the disentangled graph auto-encoder (DGAE) and the
variational disentangled graph auto-encoder (VDGAE). Our work provides a
pioneering effort to apply the disentanglement strategy to link prediction. The
proposed framework infers the latent factors that cause edges in the graph and
disentangles the representation into multiple channels corresponding to unique
latent factors, which contributes to improving the performance of link
prediction. To further encourage the embeddings to capture mutually exclusive
latent factors, we introduce mutual information regularization to enhance the
independence among different channels. Extensive experiments on various
real-world benchmarks demonstrate that our proposed methods achieve
state-of-the-art results compared to a variety of strong baselines on link
prediction tasks. Qualitative analysis on the synthetic dataset also
illustrates that the proposed methods can capture distinct latent factors that
cause links, providing empirical evidence that our models are able to explain
the results of link prediction to some extent. All code will be made publicly
available upon publication of the paper.
