Title: AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks,
Abstract: Heterogeneous information networks (HINs) are ubiquitous in real-world
applications. Due to the heterogeneity in HINs, the typed edges may not fully
align with each other. In order to capture the semantic subtlety, we propose
the concept of aspects with each aspect being a unit representing one
underlying semantic facet. Meanwhile, network embedding has emerged as a
powerful method for learning network representation, where the learned
embedding can be used as features in various downstream applications.
Therefore, we are motivated to propose a novel embedding learning
framework---AspEm---to preserve the semantic information in HINs based on
multiple aspects. Instead of preserving information of the network in one
semantic space, AspEm encapsulates information regarding each aspect
individually. In order to select aspects for embedding purpose, we further
devise a solution for AspEm based on dataset-wide statistics. To corroborate
the efficacy of AspEm, we conducted experiments on two real-words datasets with
two types of applications---classification and link prediction. Experiment
results demonstrate that AspEm can outperform baseline network embedding
learning methods by considering multiple aspects, where the aspects can be
selected from the given HIN in an unsupervised manner.