# Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

arXiv: https://arxiv.org/abs/1503.07439 · HTML: https://mlsanity.com/paper/1503.07439/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2015.

## Built on by (most cited first, 6 of 6)

- [Evading Community Detection via Counterfactual Neighborhood Search](https://mlsanity.com/paper/2310.08909/) (2023)
- [UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities](https://mlsanity.com/paper/2403.04247/) (2024)
- [Graph Neural Network Surrogates of Fair Graph Filtering](https://mlsanity.com/paper/2303.08157/) (2023)
- [Efficiently Learning Branching Networks for Multitask Algorithmic Reasoning](https://mlsanity.com/paper/2512.01113/) (2025)
- [A graph neural network based on feature network for identifying influential nodes](https://mlsanity.com/paper/2508.01278/) (2025)
- [Evading Overlapping Community Detection via Proxy Node Injection](https://mlsanity.com/paper/2509.21211/) (2025)

## Abstract (arXiv)

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.