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

Title	MOCMIN: convex inferring of modular low-rank contact networks over COVID diffusion data
Author	Sefer, Emre
Publication Date:	2022
Publication Place	- TÜBİTAK
Subject	COVID, Diffusion, Graph inference, Modularity, SEIR
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1300-0632
Record ID	6ec2e44b-cc9f-4c78-bd48-b1c5c5153654
Library Location	Computer Science
Date	2022
Sample Text	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

Author Sefer, Emre

Publication Date 2022

Publication Place - TÜBİTAK

Subject COVID, Diffusion, Graph inference, Modularity, SEIR

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1300-0632

Record ID 6ec2e44b-cc9f-4c78-bd48-b1c5c5153654

Library Location Computer Science

Date 2022

Sample Text 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