ArticleNature communications2024
Network community detection via neural embeddings.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- K-Means Community Detection Algorithm Based on Density Peaks.Entropy (Basel, Switzerland) · 2026Article
- A comprehensive framework for solution space exploration in community detection.Scientific reports · 2025Article
- Community detection in hypergraphs through hyperedge percolation.Scientific reports · 2025Article
- Integrative Transcriptomic and Network-Based Analysis of Neuromuscular Diseases.International journal of molecular sciences · 2025Article
- Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies.Briefings in bioinformatics · 2025Article
- Public Opinion Propagation Prediction Model Based on Dynamic Time-Weighted Rényi Entropy and Graph Neural Network.Entropy (Basel, Switzerland) · 2025Article
- Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.Nature communications · 2025Article
- FFrontiers in computational neuroscience · 2025Article
- Network community detection via neural embeddings.Nature communications · 2024Article
- Iterative embedding and reweighting of complex networks reveals community structure.Scientific reports · 2024Article
- Article
- Crowdsourced audit of Twitter's recommender systems.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work-particularly how network structure gets encoded in the embedding-remain largely unexplained. Here, we show that node2vec-shallow, linear neural network-encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.