Clustering social audiences in business information networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 107126 |
Journal / Publication | Pattern Recognition |
Volume | 100 |
Online published | 28 Nov 2019 |
Publication status | Published - Apr 2020 |
Link(s)
Abstract
Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.
Research Area(s)
- Business information networks, Clustering, Machine learning, Social networks
Citation Format(s)
Clustering social audiences in business information networks. / Zheng, Yu; Hu, Ruiqi; Fung, Sai-fu et al.
In: Pattern Recognition, Vol. 100, 107126, 04.2020.
In: Pattern Recognition, Vol. 100, 107126, 04.2020.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review