Mining directed social network from message board
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
A social hypertext model for finding community in blogs
Proceedings of the seventeenth conference on Hypertext and hypermedia
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Mining personal social features in the community of email users
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
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The widespread popularity and vigorous growth of micro-blogging systems provides a fertile source for analyzing social networks and phenomenon. Currently, few data mining tools can deal with unique characteristics of micro-blogging systems. In this study, we propose an integrate approach for mining user relationships in micro-blogging systems. The approach starts from macroscopic analysis of social networks by grouping users with the method of maximal strongly connected components (MSCC). Following that, a measure of condensation level of groups are calculated to find out the most influential group, and all groups can be ranked according to this measure; then a new algorithm is presented to evaluate the influence of a specific user within a group. The integrated approach is capable to analyze large amount data sets. It is useful for exploring directions of information diffusion and evaluating the scope and the strength of individual user's influence in micro-blogging systems.