# PINE: Pipeline for Important Node Exploration in Attributed Networks

arXiv: https://arxiv.org/abs/2512.07244 · HTML: https://mlsanity.com/paper/2512.07244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 79 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2007.02901/) (2020)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks](https://mlsanity.com/paper/1905.08865/) (2019)
- [PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT](https://mlsanity.com/paper/2103.11933/) (2021)
- [Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach](https://mlsanity.com/paper/1905.10418/) (2019)
- [Identifying a set of influential spreaders in complex networks](https://mlsanity.com/paper/1602.00070/) (2016)
- [Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks](https://mlsanity.com/paper/2012.15725/) (2020)
- [PhysBERT: A Text Embedding Model for Physics Scientific Literature](https://mlsanity.com/paper/2408.09574/) (2024)
- [Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization](https://mlsanity.com/paper/2503.20697/) (2025)
- [Tracing technological development trajectories: A genetic knowledge persistence-based main path approach](https://mlsanity.com/paper/1608.07371/) (2016)
- [The disruption index is biased by citation inflation](https://mlsanity.com/paper/2306.01949/) (2023)
- [Identifying Influential Brokers on Social Media from Social Network Structure](https://mlsanity.com/paper/2208.00630/) (2022)
- [RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes](https://mlsanity.com/paper/2403.06385/) (2024)

## Built on by (most cited first, 1 of 1)

- [ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment](https://mlsanity.com/paper/2603.28128/) (2026)

## Abstract (arXiv)

A graph with semantically attributed nodes are a common data structure in a wide range of domains. It could be interlinked web data or citation networks of scientific publications. The essential problem for such a data type is to determine nodes that carry greater importance than all the others, a task that markedly enhances system monitoring and management. Traditional methods to identify important nodes in networks introduce centrality measures, such as node degree or more complex PageRank. However, they consider only the network structure, neglecting the rich node attributes. Recent methods adopt neural networks capable of handling node features, but they require supervision. This work addresses the identified gap--the absence of approaches that are both unsupervised and attribute-aware--by introducing a Pipeline for Important Node Exploration (PINE). At the core of the proposed framework is an attention-based graph model that incorporates node semantic features in the learning process of identifying the structural graph properties. The PINE's node importance scores leverage the obtained attention distribution. We demonstrate the superior performance of the proposed PINE method on various homogeneous and heterogeneous attributed networks. As an industry-implemented system, PINE tackles the real-world challenge of unsupervised identification of key entities within large-scale enterprise graphs.