# Node Embedding via Word Embedding for Network Community Discovery

arXiv: https://arxiv.org/abs/1611.03028 · HTML: https://mlsanity.com/paper/1611.03028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2016.

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

- [Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings](https://mlsanity.com/paper/2109.04526/) (2021)

## Abstract (arXiv)

Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed approach consistently improves over the current state-of-the-art. Specifically, our approach empirically attains the information-theoretic limits for community recovery under the benchmark Stochastic Block Models for graph generation and exhibits better stability and accuracy over both Spectral Clustering and Acyclic Belief Propagation in the community recovery limits.