# MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

arXiv: https://arxiv.org/abs/2006.12001 · HTML: https://mlsanity.com/paper/2006.12001/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks](https://mlsanity.com/paper/2402.12411/) (2024)
- [MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning](https://mlsanity.com/paper/2206.09280/) (2022)
- [Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs](https://mlsanity.com/paper/2402.17791/) (2024)
- [GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection](https://mlsanity.com/paper/2404.01578/) (2024)
- [Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs](https://mlsanity.com/paper/2412.00478/) (2024)
- [Critical Nodes Identification in Complex Networks: A Survey](https://mlsanity.com/paper/2507.06164/) (2025)
- [Forward Learning of Graph Neural Networks](https://mlsanity.com/paper/2403.11004/) (2024)
- [Knowledge-Guided Dynamic Systems Modeling: A Case Study on Modeling River Water Quality](https://mlsanity.com/paper/2103.00792/) (2021)
- [CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation](https://mlsanity.com/paper/2402.05135/) (2024)

## Abstract (arXiv)

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.