# Personalized PageRank to a Target Node, Revisited

arXiv: https://arxiv.org/abs/2006.11876 · HTML: https://mlsanity.com/paper/2006.11876/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

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

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- [TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction](https://mlsanity.com/paper/2204.07703/) (2022)
- [BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction](https://mlsanity.com/paper/2204.09508/) (2022)
- [Local Structure-aware Graph Contrastive Representation Learning](https://mlsanity.com/paper/2308.03271/) (2023)
- [Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillation](https://mlsanity.com/paper/2210.11094/) (2022)
- [Fairness-aware PageRank via Edge Reweighting](https://mlsanity.com/paper/2512.08055/) (2025)
- [CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation](https://mlsanity.com/paper/2402.05135/) (2024)
- [Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective](https://mlsanity.com/paper/2405.19649/) (2024)
- [Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach](https://mlsanity.com/paper/2412.10789/) (2024)
- [Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks](https://mlsanity.com/paper/2609.37972/) (2026)

## Abstract (arXiv)

Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node s and a target node t, the PPR value π(s,t) represents the probability that a random walk from s terminates at t, and thus indicates the bidirectional importance between s and t. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source node s and every node t ∈ V. However, the single-source query only reflects the importance of each node t with respect to s. In this paper, we consider the em single-target PPR query, which measures the opposite direction of importance for PPR. Given a target node t, the single-target PPR query asks for the PPR value of every node s∈ V to a given target node t. We propose RBS, a novel algorithm that answers approximate single-target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.