# Vital nodes identification in complex networks

arXiv: https://arxiv.org/abs/1607.01134 · HTML: https://mlsanity.com/paper/1607.01134/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2016.

## Built on by (most cited first, 16 of 16)

- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [A Fast Algorithm for Moderating Critical Nodes via Edge Removal](https://mlsanity.com/paper/2309.06392/) (2023)
- [Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/2106.10419/) (2021)
- [Hyperparameter-free and Explainable Whole Graph Embedding](https://mlsanity.com/paper/2108.02113/) (2021)
- [Influential Simplices Mining via Simplicial Convolutional Network](https://mlsanity.com/paper/2307.05841/) (2023)
- [MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence](https://mlsanity.com/paper/2108.06216/) (2021)
- [Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach](https://mlsanity.com/paper/2202.06229/) (2022)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [QANA: LLM-based Question Generation and Network Analysis for Zero-shot Key Point Analysis and Beyond](https://mlsanity.com/paper/2404.18371/) (2024)
- [Efficient Algorithms for Computing Random Walk Centrality](https://mlsanity.com/paper/2510.20604/) (2025)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees](https://mlsanity.com/paper/2111.06968/) (2021)
- [Identifying critical nodes in complex networks by graph representation learning](https://mlsanity.com/paper/2201.07988/) (2022)
- [Network Alignment](https://mlsanity.com/paper/2504.11367/) (2025)
- [HawkesRank: Event-Driven Centrality for Real-Time Importance Ranking](https://mlsanity.com/paper/2603.11472/) (2026)
- [Testing Central Structure for Complex Networks](https://mlsanity.com/paper/2610.15659/) (2026)

## Abstract (arXiv)

Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In despite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.