Sparse Networks with Core-Periphery Structure

Naik, Cian; Caron, François; Rousseau, Judith

doi:10.48550/arxiv.1910.09679

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…We leave the question of how exactly the topology of the model edges dictate (or do not dictate) the topology of the added noise for future work. Additionally, one might ask if real-world sparse networks [6,40] have a dominant structure that protects their architecture against random fluctuations.…”

Section: Discussionmentioning

confidence: 99%

Variability in higher order structure of noise added to weighted networks

Blevins¹,

Kim²,

Bassett³

2021

Preprint

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From spiking activity in neuronal networks to force chains in granular materials, the behavior of many real-world systems depends on a network of both strong and weak interactions. These interactions give rise to complex and higher-order system behaviors, and are encoded using data as the network's edges. However, distinguishing between true weak edges and low-weight edges caused by noise remains a challenge. We address this problem by examining the higher-order structure of noisy, weak edges added to model networks. We find that the structure of low-weight, noisy edges varies according to the topology of the model network to which it is added. By investigating this variation more closely, we see that at least three qualitative classes of noise structure emerge. Furthermore, we observe that the structure of noisy edges contains enough model-specific information to classify the model networks with moderate accuracy. Finally, we offer network generation rules that can drive different types of structure in added noisy edges. Our results demonstrate that noise does not present as a monolithic nuisance, but rather as a nuanced, topologydependent, and even useful entity in characterizing higher-order network interactions. Hence, we provide an alternate approach to noise management by embracing its role in such interactions.

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Section: Discussionmentioning

confidence: 99%

Variability in higher order structure of noise added to weighted networks

Blevins¹,

Kim²,

Bassett³

2021

Preprint

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“…Meanwhile, this definition heavily relies on the density gap between the core and the periphery [Zhang et al, 2015, Kojaku andMasuda, 2018] which may not be true in many applications. Naik et al [2019] recently propose another core-periphery model. The core structure is more general than the Erdös-Rényi but still follows a restrictive parametric form.…”

Section: Introductionmentioning

confidence: 99%

Informative core identification in complex networks

Miao¹,

Li²

2021

Preprint

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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.

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Sparse Networks with Core-Periphery Structure

Cited by 2 publications

References 29 publications

Variability in higher order structure of noise added to weighted networks

Variability in higher order structure of noise added to weighted networks

Informative core identification in complex networks

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