  Given multiple input signals, how can we infer node importance in a knowledge
graph (KG)? Node importance estimation is a crucial and challenging task that
can benefit a lot of applications including recommendation, search, and query
disambiguation. A key challenge towards this goal is how to effectively use
input from different sources. On the one hand, a KG is a rich source of
information, with multiple types of nodes and edges. On the other hand, there
are external input signals, such as the number of votes or pageviews, which can
directly tell us about the importance of entities in a KG. While several
methods have been developed to tackle this problem, their use of these external
signals has been limited as they are not designed to consider multiple signals
simultaneously. In this paper, we develop an end-to-end model MultiImport,
which infers latent node importance from multiple, potentially overlapping,
input signals. MultiImport is a latent variable model that captures the
relation between node importance and input signals, and effectively learns from
multiple signals with potential conflicts. Also, MultiImport provides an
effective estimator based on attentive graph neural networks. We ran
experiments on real-world KGs to show that MultiImport handles several
challenges involved with inferring node importance from multiple input signals,
and consistently outperforms existing methods, achieving up to 23.7% higher
NDCG@100 than the state-of-the-art method.
