MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data

Name: MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data
Author: Sefer, Emre

İsim	MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data
Yazar	Sefer, Emre
Basım Tarihi:	2022
Basım Yeri	- TÜBİTAK
Konu	COVID, Diffusion, Graph inference, Modularity, SEIR
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1300-0632
Kayıt Numarası	6ec2e44b-cc9f-4c78-bd48-b1c5c5153654
Lokasyon	Computer Science
Tarih	2022
Örnek Metin	SEIR (which consists of susceptible, exposed, infected, and recovered states) is a common diffusion model which could model different disease propagation dynamics across various domains such as influenza and COVID diffusion. As a motivation, across these domains, observing the node states is relatively easier than observing the network edges over which the diffusion is taking place, or it may not even be possible to observe the underlying network. This paper focuses on the problem of predicting modular low-rank human contact network edges only if a SEIR diffusion dynamics spreading among the human on their contact network can be observed. Such contact networks exhibit high modularity where the graph has dense connections between the vertices within modules, but sparse connections between vertices in different modules. We first formulate such inference problem as an optimization problem, discuss its convexity, and propose MOCMIN to optimally infer such unknown contacts of modular human contact network from COVID diffusion data. This modular contact network inference problem is important in the general case where human states such as infected with virus and recovered from virus can be identified more easily than the contacts between humans. Our contributions can be summarized as follows: (1) MOCMIN can handle noisy, incomplete, or undersampled diffusion data while inferring the unknown contact network; (2) The inferred contact networks are highly modular which cannot be ensured by the existing methods; (3) This paper applies MOCMIN to better understand COVID diffusion on contact network. We found MOCMIN to be accurate in modular real human contact network inference from COVID diffusion data under a number of challenging scenarios. As an example, such high school contact network can be inferred by tracking COVID diffusion among humans approximately 5% better than the compared methods by MOCMIN’s ability to model modularity of the network. Via such inference, we can also understand the details of COVID diffusion dynamics in real human contact network. Additionally, inferred human contact graphs nearly mimic the true contact network’s known graphical properties. Lastly, MOCMIN outperforms the competing approaches while estimating the synthetic networks.
DOI	10.55730/1300-0632.3956
Cilt	30

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MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data

Yazar Sefer, Emre

Basım Tarihi 2022

Basım Yeri - TÜBİTAK

Konu COVID, Diffusion, Graph inference, Modularity, SEIR

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1300-0632

Kayıt Numarası 6ec2e44b-cc9f-4c78-bd48-b1c5c5153654

Lokasyon Computer Science

Tarih 2022

Örnek Metin SEIR (which consists of susceptible, exposed, infected, and recovered states) is a common diffusion model which could model different disease propagation dynamics across various domains such as influenza and COVID diffusion. As a motivation, across these domains, observing the node states is relatively easier than observing the network edges over which the diffusion is taking place, or it may not even be possible to observe the underlying network. This paper focuses on the problem of predicting modular low-rank human contact network edges only if a SEIR diffusion dynamics spreading among the human on their contact network can be observed. Such contact networks exhibit high modularity where the graph has dense connections between the vertices within modules, but sparse connections between vertices in different modules. We first formulate such inference problem as an optimization problem, discuss its convexity, and propose MOCMIN to optimally infer such unknown contacts of modular human contact network from COVID diffusion data. This modular contact network inference problem is important in the general case where human states such as infected with virus and recovered from virus can be identified more easily than the contacts between humans. Our contributions can be summarized as follows: (1) MOCMIN can handle noisy, incomplete, or undersampled diffusion data while inferring the unknown contact network; (2) The inferred contact networks are highly modular which cannot be ensured by the existing methods; (3) This paper applies MOCMIN to better understand COVID diffusion on contact network. We found MOCMIN to be accurate in modular real human contact network inference from COVID diffusion data under a number of challenging scenarios. As an example, such high school contact network can be inferred by tracking COVID diffusion among humans approximately 5% better than the compared methods by MOCMIN’s ability to model modularity of the network. Via such inference, we can also understand the details of COVID diffusion dynamics in real human contact network. Additionally, inferred human contact graphs nearly mimic the true contact network’s known graphical properties. Lastly, MOCMIN outperforms the competing approaches while estimating the synthetic networks.

DOI 10.55730/1300-0632.3956

Cilt 30