Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Oliveira, MDB (Author) Other; Guerreiro, A (Author) Other; João Gama (Author) FEP

Journal

Title: Social Network Analysis and Mining

Vol. 4

ISSN: 1869-5450

Publisher: Springer Nature

Indexing

Scopus - 9 Citations

Other information

Authenticus ID: P-009-TF2

DOI: 10.1007/s13278-014-0208-2

Abstract (EN): The widespread availability of Customer Relationship Management applications in modern organizations, allows companies to collect and store vast amounts of high-detailed customer-related data. Making sense of these data using appropriate methods can yield insights into customers¿ behaviour and preferences. The extracted knowledge can then be explored for marketing purposes. Social Network Analysis techniques can play a key role in business analytics. By modelling the implicit relationships among customers as a social network, it is possible to understand how patterns in these relationships translate into competitive advantages for the company. Additionally, the incorporation of the temporal dimension in such analysis can help detect market trends and changes in customers¿ preferences. In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two different time window models: a landmark and a sliding window. Landmark windows keep all the historical data and treat all nodes and links equally, even if they only appear at the early stages of the network life. Such approach is appropriate for the long-term analysis of networks, but may fail to provide a realistic picture of the current evolution. On the other hand, sliding windows focus on the most recent past thus allowing to capture current events. The application of the proposed methodology on a real-world customer network suggests that both window models provide complementary information. Nevertheless, the sliding window model is able to capture better the recent changes of the network.

Language: English

Type (Professor's evaluation): Scientific