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.