Co-clustering enterprise social networks

Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
Subtitle of host publication24 - 29 July 2016 Vancouver, Canada
EditorsPablo A. Estevez
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages107-114
Number of pages8
ISBN (Electronic)9781509006199, 9781509006205
ISBN (Print)781509006212
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2016 - Vancouver BC, Canada
Duration: 24 Jul 201629 Jul 2016
https://ewh.ieee.org/conf/wcci/2016/

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2016
Abbreviated titleIJCNN 2016
CountryCanada
CityVancouver BC
Period24/07/1629/07/16
Internet address

Cite this

Hu, R., Pan, S., Long, G., Zhu, X., Jiang, J., & Zhang, C. (2016). Co-clustering enterprise social networks. In P. A. Estevez (Ed.), 2016 International Joint Conference on Neural Networks (IJCNN): 24 - 29 July 2016 Vancouver, Canada (pp. 107-114). [7727187] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2016.7727187
Hu, Ruiqi ; Pan, Shirui ; Long, Guodong ; Zhu, Xingquan ; Jiang, Jing ; Zhang, Chengqi. / Co-clustering enterprise social networks. 2016 International Joint Conference on Neural Networks (IJCNN): 24 - 29 July 2016 Vancouver, Canada. editor / Pablo A. Estevez. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 107-114
@inproceedings{c0afc1a5263f4b3b9d932bddb40d9fde,
title = "Co-clustering enterprise social networks",
abstract = "An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.",
author = "Ruiqi Hu and Shirui Pan and Guodong Long and Xingquan Zhu and Jing Jiang and Chengqi Zhang",
year = "2016",
doi = "10.1109/IJCNN.2016.7727187",
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Hu, R, Pan, S, Long, G, Zhu, X, Jiang, J & Zhang, C 2016, Co-clustering enterprise social networks. in P A. Estevez (ed.), 2016 International Joint Conference on Neural Networks (IJCNN): 24 - 29 July 2016 Vancouver, Canada., 7727187, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 107-114, IEEE International Joint Conference on Neural Networks 2016, Vancouver BC, Canada, 24/07/16. https://doi.org/10.1109/IJCNN.2016.7727187

Co-clustering enterprise social networks. / Hu, Ruiqi; Pan, Shirui; Long, Guodong; Zhu, Xingquan; Jiang, Jing; Zhang, Chengqi.

2016 International Joint Conference on Neural Networks (IJCNN): 24 - 29 July 2016 Vancouver, Canada. ed. / Pablo A. Estevez. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 107-114 7727187.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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T1 - Co-clustering enterprise social networks

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AU - Pan, Shirui

AU - Long, Guodong

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AU - Jiang, Jing

AU - Zhang, Chengqi

PY - 2016

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N2 - An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.

AB - An enterprise social network (ESN) involves diversified user groups from producers, suppliers, logistics, to end consumers, and users have different scales, broad interests, and various objectives, such as advertising, branding, customer relationship management etc. In addition, such a highly diversified network is also featured with rich content, including recruiting messages, advertisements, news release, customer complains etc. Due to such complex nature, an immediate need is to properly organize a chaotic enterprise social network as functional groups, where each group corresponds to a set of peers with business interactions and common objectives, and further understand the business role of each group, such as their common interests and key features differing from other groups. In this paper, we argue that due to unique characteristics of enterprise social networks, simple clustering for ESN nodes or using existing topic discovery methods cannot effectively discover functional groups and understand their roles. Alternatively, we propose CENFLD, which carries out co-clustering on enterprise social networks for functional group discovery and understanding. CENFLD is a co-factorization based framework which combines network topology structures and rich content information, including interactions between nodes and correlations between node content, to discover functional user groups. Because the number of functional groups is highly data driven and hard to estimate, CENFLD employs a hold-out test principle to find the group number optimally complying with the underlying data. Experiments and comparisons, with state-of-the-art approaches, on 13 real-world enterprise/organizational networks validate the performance of CENFLD.

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DO - 10.1109/IJCNN.2016.7727187

M3 - Conference Paper

SN - 781509006212

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BT - 2016 International Joint Conference on Neural Networks (IJCNN)

A2 - A. Estevez, Pablo

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

ER -

Hu R, Pan S, Long G, Zhu X, Jiang J, Zhang C. Co-clustering enterprise social networks. In A. Estevez P, editor, 2016 International Joint Conference on Neural Networks (IJCNN): 24 - 29 July 2016 Vancouver, Canada. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 107-114. 7727187 https://doi.org/10.1109/IJCNN.2016.7727187