Skip to main content
padlock icon - secure page this page is secure


Buy Article:

$60.00 + tax (Refund Policy)

This article uses an innovative approach for the identification of groups of users in a social network by solving a graph coloring problem (GCP). We focus on social networks of users of a company's intranet. We want to identify user groups sharing the same behavior while interacting with the enterprise resource planning (ERP) as a means to verify that their roles, work practices, and positions held in the company are correct for each user. From the ERP logs, we generate the internal social network (ISN) graph where each vertex corresponds to a user and edges correspond to relations between users. Two users will be related if they frequently use the same programs. Other user attributes like the role assigned by the administrator, workplace location, or department offer secondary information to create the relations between users. After generating the graph we identify the users' groups solving a GCP on the ISN complementary graph. Moreover, ISN graph topology changes over time, so the composition of the groups of users may also change. New users can enter the intranet and exit it, and the behavior of the users may change. Therefore, we need approaches the quickly provide user clustering solutions to be able to adapt to the ISN time changes. We propose the gravitational swarm for graph coloring (GSGC) for this task and compare it with state-of-the-art approaches. We provide results on real-life (anonymized) cases.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: graph coloring; identification; nonstationary social networks

Document Type: Research Article

Affiliations: 1: Informática 68 Investigación y Desarrollo S.L., San Sebastian, Spain, 2: Computational Intelligence Group–University of the Basque Country, UPV/EHU, San Sebastian, Spain,

Publication date: October 3, 2013

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more