Structural analysis of hypertexts: identifying hierarchies and useful metrics
ACM Transactions on Information Systems (TOIS)
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Friendster and publicly articulated social networking
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Scan Statistics on Enron Graphs
Computational & Mathematical Organization Theory
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Self-Organization Patterns in Wasp and Open Source Communities
IEEE Intelligent Systems
ACM Transactions on Internet Technology (TOIT)
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Trust and Reputation in Dynamic Scientific Communities
IEEE Distributed Systems Online
On utilising social networks to discover representatives of human communities
International Journal of Intelligent Information and Database Systems
Rethinking email message and people search
Proceedings of the 18th international conference on World wide web
User position measures in social networks
Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Mining social relationships in micro-blogging systems
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
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The development of structure analysis that constitutes the core part of social network analysis is continuously supported by the rapid expansion of different kinds of social networks available in the Internet. The network analyzed in this paper is built based on the email communication between people. Exploiting the data about this communication some personal social features can be discovered, including personal position that means individual importance within the community. The evaluation of position of an individual is crucial for user ranking and extraction of key network members. The new method of personal importance analysis is presented in the paper. It takes into account the strength of relationships between network members, its dynamic as well as personal position of the nearest neighbours. The requirements for the commitment function that reflects the strength of the relationship are also specified. In order to validate the proposed method, the dataset containing Enron emails is utilized; first to build the virtual social network and afterwards to assess the position of the network members.