KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Topic-based social network analysis for virtual communities of interests in the Dark Web
ACM SIGKDD Workshop on Intelligence and Security Informatics
Social feature-based enterprise email classification without examining email contents
Journal of Network and Computer Applications
Leveraging social network analysis with topic models and the Semantic Web extended
Web Intelligence and Agent Systems - Web Intelligence and Communities
| Hi-index | 0.00 |
The availability of a large corpus of emails in organizations, such as the Enron dataset (used in this work), is the motivation for this work. The attempt is to see if one can predict the organizational structure of Enron by using data mining algorithms and methodologies on this email dataset. The primary approach in this attempt is the analysis of email flows within the organization. Our results show that significant information about an organization's structure can be obtained even if the body (content) of emails is neglected. Enough relevant data is extracted about the 'email' social network using simple email flow analysis and associated statistics gaining an over all picture of the organizational structure. The longer term objective of this work is to show that readily available information can be used to determine relevant metrics by which one can reconstruct and verify the approximate social hierarchies within an organization or company.