# Community detection in networks: Structural communities versus ground truth

arXiv: https://arxiv.org/abs/1406.0146 · HTML: https://mlsanity.com/paper/1406.0146/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2014.

## Built on by (most cited first, 14 of 14)

- [20 years of network community detection](https://mlsanity.com/paper/2208.00111/) (2022)
- [Effective and Scalable Clustering on Massive Attributed Graphs](https://mlsanity.com/paper/2102.03826/) (2021)
- [DINE: Dimensional Interpretability of Node Embeddings](https://mlsanity.com/paper/2310.01162/) (2023)
- [Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity](https://mlsanity.com/paper/2209.04562/) (2022)
- [Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics](https://mlsanity.com/paper/2305.02381/) (2023)
- [Multilayer Networks for Text Analysis with Multiple Data Types](https://mlsanity.com/paper/2106.15821/) (2021)
- [Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection](https://mlsanity.com/paper/2210.09186/) (2022)
- [Graph Neural Network Surrogates of Fair Graph Filtering](https://mlsanity.com/paper/2303.08157/) (2023)
- [Disentangled and Self-Explainable Node Representation Learning](https://mlsanity.com/paper/2410.21043/) (2024)
- [Overcoming Bias in Community Detection Evaluation](https://mlsanity.com/paper/2102.03472/) (2021)
- [An Effective Index for Truss-based Community Search on Large Directed Graphs](https://mlsanity.com/paper/2401.10641/) (2024)
- [Robust Markov stability for community detection at a scale learned based on the structure](https://mlsanity.com/paper/2504.11621/) (2025)
- [Unifying Information-Theoretic and Pair-Counting Clustering Similarity](https://mlsanity.com/paper/2511.03000/) (2025)
- [Community Concealment from Graph Neural Networks](https://mlsanity.com/paper/2602.12250/) (2026)

## Abstract (arXiv)

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.