# Improving PageRank for Local Community Detection

arXiv: https://arxiv.org/abs/1610.08722 · HTML: https://mlsanity.com/paper/1610.08722/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

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

- [Unnoticeable Community Deception via Multi-objective Optimization](https://mlsanity.com/paper/2509.01438/) (2025)

## Abstract (arXiv)

Community detection is a classical problem in the field of graph mining. While most algorithms work on the entire graph, it is often interesting in practice to recover only the community containing some given set of seed nodes. In this paper, we propose a novel approach to this problem, using some low-dimensional embedding of the graph based on random walks starting from the seed nodes. From this embedding, we propose some simple yet efficient versions of the PageRank algorithm as well as a novel algorithm, called WalkSCAN, that is able to detect multiple communities, possibly overlapping. We provide insights into the performance of these algorithms through the theoretical analysis of a toy network and show that WalkSCAN outperforms existing algorithms on real networks.