# Informative core identification in complex networks

arXiv: https://arxiv.org/abs/2101.06388 · HTML: https://mlsanity.com/paper/2101.06388/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 58 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [Consistency of spectral clustering in stochastic block models](https://mlsanity.com/paper/1312.2050/) (2013)
- [Statistical inference on random dot product graphs: a survey](https://mlsanity.com/paper/1709.05454/) (2017)
- [Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel](https://mlsanity.com/paper/1309.4111/) (2013)
- [Achieving Optimal Misclassification Proportion in Stochastic Block Model](https://mlsanity.com/paper/1505.03772/) (2015)
- [Estimating network edge probabilities by neighborhood smoothing](https://mlsanity.com/paper/1509.08588/) (2015)
- [Incoherence-Optimal Matrix Completion](https://mlsanity.com/paper/1310.0154/) (2013)
- [Network cross-validation by edge sampling](https://mlsanity.com/paper/1612.04717/) (2016)
- [On a 'Two Truths' Phenomenon in Spectral Graph Clustering](https://mlsanity.com/paper/1808.07801/) (2018)
- [Coauthorship and Citation Networks for Statisticians](https://mlsanity.com/paper/1410.2840/) (2014)
- [Information Limits for Recovering a Hidden Community](https://mlsanity.com/paper/1509.07859/) (2015)
- [Spectral Algorithms for Community Detection in Directed Networks](https://mlsanity.com/paper/2008.03820/) (2020)
- [Hierarchical community detection by recursive partitioning](https://mlsanity.com/paper/1810.01509/) (2018)
- [Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix](https://mlsanity.com/paper/1502.01988/) (2015)
- [Identification of core-periphery structure in networks](https://mlsanity.com/paper/1409.4813/) (2014)
- [Community detection in networks: Modularity optimization and maximum likelihood are equivalent](https://mlsanity.com/paper/1606.02319/) (2016)
- [Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems](https://mlsanity.com/paper/1502.06590/) (2015)
- [Testing for Global Network Structure Using Small Subgraph Statistics](https://mlsanity.com/paper/1710.00862/) (2017)
- [Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections](https://mlsanity.com/paper/1304.1548/) (2013)
- [Detection of Core-Periphery Structure in Networks Using Spectral Methods and Geodesic Paths](https://mlsanity.com/paper/1410.6572/) (2014)
- [Consistency of Spectral Clustering on Hierarchical Stochastic Block Models](https://mlsanity.com/paper/2004.14531/) (2020)
- [Core-periphery structure requires something else in the network](https://mlsanity.com/paper/1710.07076/) (2017)
- [Density-based and transport-based core-periphery structures in networks](https://mlsanity.com/paper/1310.8387/) (2013)
- [Centrality metrics and localization in core-periphery networks](https://mlsanity.com/paper/1510.01116/) (2015)
- [Community models for networks observed through edge nominations](https://mlsanity.com/paper/2008.03652/) (2020)

## Built on by (most cited first, 3 of 3)

- [Diffusion Source Identification on Networks with Statistical Confidence](https://mlsanity.com/paper/2106.04800/) (2021)
- [Network Estimation by Mixing: Adaptivity and More](https://mlsanity.com/paper/2106.02803/) (2021)
- [Community Detection in Weighted Multilayer Networks with Ambient Noise](https://mlsanity.com/paper/2103.00486/) (2021)

## Abstract (arXiv)

In network analysis, the core structure of modeling interest is usually hidden in a larger network in which most structures are not informative. The noise and bias introduced by the non-informative component in networks can obscure the salient structure and limit many network modeling procedures' effectiveness. This paper introduces a novel core-periphery model for the non-informative periphery structure of networks without imposing a specific form for the informative core structure. We propose spectral algorithms for core identification as a data preprocessing step for general downstream network analysis tasks based on the model. The algorithm enjoys a strong theoretical guarantee of accuracy and is scalable for large networks. We evaluate the proposed method by extensive simulation studies demonstrating various advantages over many traditional core-periphery methods. The method is applied to extract the informative core structure from a citation network and give more informative results in the downstream hierarchical community detection.