Segregation in social networks: Markov bridge models and estimation

Vikram Krishnamurthy; Rui Luo; Buddhika Nettasinghe

doi:10.1109/ICASSP39728.2021.9413690

Segregation in social networks: Markov bridge models and estimation

Vikram Krishnamurthy, Rui Luo, Buddhika Nettasinghe

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

This paper deals with the modeling and estimation of the sociological phenomena called segregation in social networks. Specifically, we present a novel community-based graph model that represent segregation as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. Based on the proposed model, we provide Bayesian filtering algorithms for recursively estimating the level of segregation using noisy samples obtained from the graph. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required. ©2021 IEEE

Original language	English
Title of host publication	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	5484-5488
Number of pages	5
ISBN (Electronic)	978-1-7281-7605-5
ISBN (Print)	978-1-7281-7606-2
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9413690
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021) - Virtual, Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021 Conference number: 46th https://www.2021.ieeeicassp.org/2021.ieeeicassp.org/index.html

Publication series

Name
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021)
Place	Canada
City	Toronto
Period	6/06/21 → 11/06/21
Internet address	https://www.2021.ieeeicassp.org/2021.ieeeicassp.org/index.html

Funding

This work was supported in part by National Science Foundation under grant ENG 60064237 and 1714180, and the U. S. Army Research Office under grant W911NF-19-1-0365.

Research Keywords

Bayesian filtering
Markov bridge
Schelling’s model
Segregation
Social networks

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10.1109/ICASSP39728.2021.9413690

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@inproceedings{25e9706fc42e45efa2352a02ff3e2c74,

title = "Segregation in social networks: Markov bridge models and estimation",

abstract = "This paper deals with the modeling and estimation of the sociological phenomena called segregation in social networks. Specifically, we present a novel community-based graph model that represent segregation as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. Based on the proposed model, we provide Bayesian filtering algorithms for recursively estimating the level of segregation using noisy samples obtained from the graph. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required. {\textcopyright}2021 IEEE",

keywords = "Bayesian filtering, Markov bridge, Schelling{\textquoteright}s model, Segregation, Social networks",

author = "Vikram Krishnamurthy and Rui Luo and Buddhika Nettasinghe",

year = "2021",

doi = "10.1109/ICASSP39728.2021.9413690",

language = "English",

isbn = "978-1-7281-7606-2",

publisher = "IEEE",

pages = "5484--5488",

booktitle = "2021 IEEE International Conference on Acoustics, Speech, and Signal Processing",

address = "United States",

note = "2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021) ; Conference date: 06-06-2021 Through 11-06-2021",

url = "https://www.2021.ieeeicassp.org/2021.ieeeicassp.org/index.html",

}

Krishnamurthy, V, Luo, R & Nettasinghe, B 2021, Segregation in social networks: Markov bridge models and estimation. in 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings. IEEE, pp. 5484-5488, 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, 6/06/21. https://doi.org/10.1109/ICASSP39728.2021.9413690

Segregation in social networks: Markov bridge models and estimation. / Krishnamurthy, Vikram; Luo, Rui; Nettasinghe, Buddhika.
2021 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings. IEEE, 2021. p. 5484-5488.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Segregation in social networks

T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021)

AU - Krishnamurthy, Vikram

AU - Luo, Rui

AU - Nettasinghe, Buddhika

N1 - Conference code: 46th

PY - 2021

Y1 - 2021

N2 - This paper deals with the modeling and estimation of the sociological phenomena called segregation in social networks. Specifically, we present a novel community-based graph model that represent segregation as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. Based on the proposed model, we provide Bayesian filtering algorithms for recursively estimating the level of segregation using noisy samples obtained from the graph. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required. ©2021 IEEE

AB - This paper deals with the modeling and estimation of the sociological phenomena called segregation in social networks. Specifically, we present a novel community-based graph model that represent segregation as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. Based on the proposed model, we provide Bayesian filtering algorithms for recursively estimating the level of segregation using noisy samples obtained from the graph. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required. ©2021 IEEE

KW - Bayesian filtering

KW - Markov bridge

KW - Schelling’s model

KW - Segregation

KW - Social networks

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U2 - 10.1109/ICASSP39728.2021.9413690

DO - 10.1109/ICASSP39728.2021.9413690

M3 - RGC 32 - Refereed conference paper (with host publication)

AN - SCOPUS:85115109629

SN - 978-1-7281-7606-2

SP - 5484

EP - 5488

BT - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing

PB - IEEE

Y2 - 6 June 2021 through 11 June 2021

ER -

Segregation in social networks: Markov bridge models and estimation

Abstract

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