Influence based analysis of community consistency in dynamic networks

Xiaowei Jia; Xiaoyi Li; Nan Du; Yuan Zhang; Vishrawas Gopalakrishnan; Guangxu Xun; Aidong Zhang

doi:10.1109/ASONAM.2016.7752206

Influence based analysis of community consistency in dynamic networks

Xiaowei Jia
, Xiaoyi Li
, Nan Du
, Yuan Zhang
, Vishrawas Gopalakrishnan
, Guangxu Xun
, Aidong Zhang

Department of Computer Science and Engineering

University of Minnesota Twin Cities
SUNY Buffalo
North Carolina State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Editors	Ravi Kumar, James Caverlee, Hanghang Tong
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781509028467
DOIs	https://doi.org/10.1109/ASONAM.2016.7752206
State	Published - Nov 21 2016
Event	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States Duration: Aug 18 2016 → Aug 21 2016

Publication series

Name	Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Conference

Conference	2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
Country/Territory	United States
City	San Francisco
Period	08/18/16 → 08/21/16

Access to Document

10.1109/ASONAM.2016.7752206

Cite this

Jia, X., Li, X., Du, N., Zhang, Y., Gopalakrishnan, V., Xun, G., & Zhang, A. (2016). Influence based analysis of community consistency in dynamic networks. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 1-8). Article 7752206 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752206

Jia, Xiaowei ; Li, Xiaoyi ; Du, Nan et al. / Influence based analysis of community consistency in dynamic networks. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. editor / Ravi Kumar ; James Caverlee ; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1-8 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

@inproceedings{4c43998e629c415aaf425cda8c6ed542,

title = "Influence based analysis of community consistency in dynamic networks",

abstract = "The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.",

author = "Xiaowei Jia and Xiaoyi Li and Nan Du and Yuan Zhang and Vishrawas Gopalakrishnan and Guangxu Xun and Aidong Zhang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

year = "2016",

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day = "21",

doi = "10.1109/ASONAM.2016.7752206",

language = "English",

series = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--8",

editor = "Ravi Kumar and James Caverlee and Hanghang Tong",

booktitle = "Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016",

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Jia, X, Li, X, Du, N, Zhang, Y, Gopalakrishnan, V, Xun, G & Zhang, A 2016, Influence based analysis of community consistency in dynamic networks. in R Kumar, J Caverlee & H Tong (eds), Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752206, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 08/18/16. https://doi.org/10.1109/ASONAM.2016.7752206

Influence based analysis of community consistency in dynamic networks. / Jia, Xiaowei; Li, Xiaoyi; Du, Nan et al.
Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. ed. / Ravi Kumar; James Caverlee; Hanghang Tong. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1-8 7752206 (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Influence based analysis of community consistency in dynamic networks

AU - Jia, Xiaowei

AU - Li, Xiaoyi

AU - Du, Nan

AU - Zhang, Yuan

AU - Gopalakrishnan, Vishrawas

AU - Xun, Guangxu

AU - Zhang, Aidong

PY - 2016/11/21

Y1 - 2016/11/21

N2 - The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.

AB - The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.

UR - https://www.scopus.com/pages/publications/85006716788

U2 - 10.1109/ASONAM.2016.7752206

DO - 10.1109/ASONAM.2016.7752206

M3 - Conference contribution

AN - SCOPUS:85006716788

T3 - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

SP - 1

EP - 8

BT - Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

A2 - Kumar, Ravi

A2 - Caverlee, James

A2 - Tong, Hanghang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

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ER -

Jia X, Li X, Du N, Zhang Y, Gopalakrishnan V, Xun G et al. Influence based analysis of community consistency in dynamic networks. In Kumar R, Caverlee J, Tong H, editors, Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1-8. 7752206. (Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016). doi: 10.1109/ASONAM.2016.7752206

Influence based analysis of community consistency in dynamic networks

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