Authors:
Vítor Cerqueira
;
Márcia Oliveira
and
João Gama
Affiliation:
LIAAD/INESC TEC, Portugal
Keyword(s):
Community Dynamics, Community Selection, Network Sampling, Large-scale Networks, Social Networks, CDR Data.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Data Mining
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Large Scale Databases
;
Mobile Databases
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Telecommunications companies must process large-scale social networks that reveal the communication patterns
among their customers. These networks are dynamic in nature as new customers appear, old customers
leave, and the interaction among customers changes over time. One way to uncover the evolution patterns of
such entities is by monitoring the evolution of the communities they belong to. Large-scale networks typically
comprise thousands, or hundreds of thousands, of communities and not all of them are worth monitoring, or
interesting from the business perspective. Several methods have been proposed for tracking the evolution of
groups of entities in dynamic networks but these methods lack strategies to effectively extract knowledge and
insight from the analysis. In this paper we tackle this problem by proposing an integrated business-oriented
framework to track and interpret the evolution of communities in very large networks. The framework encompasses
several steps such as netwo
rk sampling, community detection, community selection, monitoring
of dynamic communities and rule-based interpretation of community evolutionary profiles. The usefulness of
the proposed framework is illustrated using a real-world large-scale social network from a major telecommunications
company.
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