# PageRank beyond the Web

arXiv: https://arxiv.org/abs/1407.5107 · HTML: https://mlsanity.com/paper/1407.5107/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2014.

## Built on by (most cited first, 30 of 51)

- [Unlearning Graph Classifiers with Limited Data Resources](https://mlsanity.com/paper/2211.03216/) (2022)
- [GraphHD: Efficient graph classification using hyperdimensional computing](https://mlsanity.com/paper/2205.07826/) (2022)
- [What Can We Learn from State Space Models for Machine Learning on Graphs?](https://mlsanity.com/paper/2406.05815/) (2024)
- [Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank](https://mlsanity.com/paper/2207.06944/) (2022)
- [DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy](https://mlsanity.com/paper/2210.04442/) (2022)
- [EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection](https://mlsanity.com/paper/2603.00155/) (2026)
- [MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations](https://mlsanity.com/paper/2104.03895/) (2021)
- [MGC: A Complex-Valued Graph Convolutional Network for Directed Graphs](https://mlsanity.com/paper/2110.07570/) (2021)
- [Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning](https://mlsanity.com/paper/2106.05065/) (2021)
- [Differentially Private Graph Diffusion with Applications in Personalized PageRanks](https://mlsanity.com/paper/2407.00077/) (2024)
- [Intrinsically motivated graph exploration using network theories of human curiosity](https://mlsanity.com/paper/2307.04962/) (2023)
- [Exact and rapid linear clustering of networks with dynamic programming](https://mlsanity.com/paper/2301.10403/) (2023)
- [Improved theoretical guarantee for rank aggregation via spectral method](https://mlsanity.com/paper/2309.03808/) (2023)
- [The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks](https://mlsanity.com/paper/2310.01144/) (2023)
- [Local Graph Clustering with Noisy Labels](https://mlsanity.com/paper/2310.08031/) (2023)
- [Faster Local Solvers for Graph Diffusion Equations](https://mlsanity.com/paper/2410.21634/) (2024)
- [A Graph Based Raman Spectral Processing Technique for Exosome Classification](https://mlsanity.com/paper/2504.15324/) (2025)
- [Topological structure of complex predictions](https://mlsanity.com/paper/2207.14358/) (2022)
- [A Network Science perspective of Graph Convolutional Networks: A survey](https://mlsanity.com/paper/2301.04824/) (2023)
- [Synthetic Text Generation using Hypergraph Representations](https://mlsanity.com/paper/2309.06550/) (2023)
- [MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length](https://mlsanity.com/paper/2409.10263/) (2024)
- [Invariant Measures for Data-Driven Dynamical System Identification: Analysis and Application](https://mlsanity.com/paper/2502.05204/) (2025)
- [Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima](https://mlsanity.com/paper/2505.03717/) (2025)
- [CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration](https://mlsanity.com/paper/2506.14843/) (2025)
- [Internet 3.0: Architecture for a Web-of-Agents with it's Algorithm for Ranking Agents](https://mlsanity.com/paper/2509.04979/) (2025)
- [Efficient Algorithms for Computing Random Walk Centrality](https://mlsanity.com/paper/2510.20604/) (2025)
- [Fairness-aware PageRank via Edge Reweighting](https://mlsanity.com/paper/2512.08055/) (2025)
- [Promoting Fairness in Information Access within Social Networks](https://mlsanity.com/paper/2512.14711/) (2025)
- [Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining](https://mlsanity.com/paper/2601.02806/) (2026)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)

## Abstract (arXiv)

Google's PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We'll see the mathematics and ideas that unite these diverse applications.