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 language | English |
|---|---|
| Title of host publication | 2016 International Joint Conference on Neural Networks (IJCNN) |
| Subtitle of host publication | 24 - 29 July 2016 Vancouver, Canada |
| Editors | Pablo A. Estevez |
| Place of Publication | Piscataway NJ USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 107-114 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509006199, 9781509006205 |
| ISBN (Print) | 781509006212 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | IEEE International Joint Conference on Neural Networks 2016 - Vancouver BC, Canada Duration: 24 Jul 2016 → 29 Jul 2016 https://ewh.ieee.org/conf/wcci/2016/ |
Conference
| Conference | IEEE International Joint Conference on Neural Networks 2016 |
|---|---|
| Abbreviated title | IJCNN 2016 |
| Country | Canada |
| City | Vancouver BC |
| Period | 24/07/16 → 29/07/16 |
| Internet address |
Cite this
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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 proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Co-clustering enterprise social networks
AU - Hu, Ruiqi
AU - Pan, Shirui
AU - Long, Guodong
AU - Zhu, Xingquan
AU - Jiang, Jing
AU - Zhang, Chengqi
PY - 2016
Y1 - 2016
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.
UR - http://www.scopus.com/inward/record.url?scp=85007152011&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2016.7727187
DO - 10.1109/IJCNN.2016.7727187
M3 - Conference Paper
SN - 781509006212
SP - 107
EP - 114
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 -