# Panther: Fast Top-k Similarity Search in Large Networks

arXiv: https://arxiv.org/abs/1504.02577 · HTML: https://mlsanity.com/paper/1504.02577/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2015.

## Built on by (most cited first, 4 of 4)

- [Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures](https://mlsanity.com/paper/2404.14942/) (2024)
- [Spectral Augmentations for Graph Contrastive Learning](https://mlsanity.com/paper/2302.02909/) (2023)
- [Adaptive Local Clustering over Attributed Graphs](https://mlsanity.com/paper/2503.20488/) (2025)
- [Role Similarity Metric Based on Spanning Rooted Forest](https://mlsanity.com/paper/2110.07872/) (2021)

## Abstract (arXiv)

Estimating similarity between vertices is a fundamental issue in network analysis across various domains, such as social networks and biological networks. Methods based on common neighbors and structural contexts have received much attention. However, both categories of methods are difficult to scale up to handle large networks (with billions of nodes). In this paper, we propose a sampling method that provably and accurately estimates the similarity between vertices. The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected. We provide theoretical proofs for the error-bound and confidence of the proposed algorithm. We perform extensive empirical study and show that our algorithm can obtain top-k similar vertices for any vertex in a network approximately 300x faster than state-of-the-art methods. We also use identity resolution and structural hole spanner finding, two important applications in social networks, to evaluate the accuracy of the estimated similarities. Our experimental results demonstrate that the proposed algorithm achieves clearly better performance than several alternative methods.