# Network cross-validation by edge sampling

arXiv: https://arxiv.org/abs/1612.04717 · HTML: https://mlsanity.com/paper/1612.04717/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2016.

## Built on by (most cited first, 26 of 26)

- [Community Detection in General Hypergraph via Graph Embedding](https://mlsanity.com/paper/2103.15035/) (2021)
- [Estimating Graph Dimension with Cross-validated Eigenvalues](https://mlsanity.com/paper/2108.03336/) (2021)
- [Hypothesis Testing for Equality of Latent Positions in Random Graphs](https://mlsanity.com/paper/2105.10838/) (2021)
- [Distribution-Free Matrix Prediction Under Arbitrary Missing Pattern](https://mlsanity.com/paper/2305.11640/) (2023)
- [Network Estimation by Mixing: Adaptivity and More](https://mlsanity.com/paper/2106.02803/) (2021)
- [Informative core identification in complex networks](https://mlsanity.com/paper/2101.06388/) (2021)
- [Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels](https://mlsanity.com/paper/2208.08638/) (2022)
- [Privacy-Preserving Community Detection for Locally Distributed Multiple Networks](https://mlsanity.com/paper/2306.15709/) (2023)
- [Identifying Peer Influence in Therapeutic Communities Adjusting for Latent Homophily](https://mlsanity.com/paper/2203.14223/) (2022)
- [SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals](https://mlsanity.com/paper/2211.00128/) (2022)
- [LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks](https://mlsanity.com/paper/2505.14867/) (2025)
- [Strongly Consistent Community Detection in Popularity Adjusted Block Models](https://mlsanity.com/paper/2506.07224/) (2025)
- [Transfer Learning on Edge Connecting Probability Estimation under Graphon Model](https://mlsanity.com/paper/2510.05527/) (2025)
- [Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models](https://mlsanity.com/paper/2605.11142/) (2026)
- [Block Dense Weighted Networks with Augmented Degree Correction](https://mlsanity.com/paper/2105.12290/) (2021)
- [Spectral goodness-of-fit tests for complete and partial network data](https://mlsanity.com/paper/2106.09702/) (2021)
- [Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies](https://mlsanity.com/paper/2207.09324/) (2022)
- [Variational Inference: Posterior Threshold Improves Network Clustering Accuracy in Sparse Regimes](https://mlsanity.com/paper/2301.04771/) (2023)
- [Turning hazardous volatile matter compounds into fuel by catalytic steam reforming: An evolutionary machine learning approach](https://mlsanity.com/paper/2308.05750/) (2023)
- [Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models](https://mlsanity.com/paper/2406.05340/) (2024)
- [Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings](https://mlsanity.com/paper/2409.17544/) (2024)
- [Simultaneous estimation of connectivity and dimensionality in samples of networks](https://mlsanity.com/paper/2508.12483/) (2025)
- [Transfer learning under latent space model](https://mlsanity.com/paper/2509.15797/) (2025)
- [Adjacency Spectral Embeddings of Correlation Networks](https://mlsanity.com/paper/2602.21055/) (2026)
- [How many asymmetric communities are there in multi-layer directed networks?](https://mlsanity.com/paper/2602.21569/) (2026)
- [Nonparametric estimation of time-varying network connections by multi-stage smoothing](https://mlsanity.com/paper/2605.06862/) (2026)

## Abstract (arXiv)

While many statistical models and methods are now available for network analysis, resampling network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network resampling strategy based on splitting node pairs rather than nodes applicable to cross-validation for a wide range of network model selection tasks. We provide a theoretical justification for our method in a general setting and examples of how our method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a citation network of statisticians show that this cross-validation approach works well for model selection.