Difference between revisions of "Tang et al., TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2010"
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| − | + | This [[Category::paper]] tries to identify communities in multi-mode networks (networks which contain heterogeneous types of actors and different interactions between these actors). Other goal of this paper is to detect and identify the changes between community memberships and the interactions between actors during time. The authors used a temporally-regularized framework to solve this problem. They examined their framework on both synthetic and real datasets and obtained good results in efficiency and generality of their method. | |
Revision as of 01:30, 6 February 2011
Contents
Citation
Authors: Lei Tang, Huan Liu, and Jianping Zhang
Title: Identifying Evolving Groups in Dynamic Multi-Mode Networks
Journal: TRANSACTION ON KNOWLEDGE AND DATA ENGINEERING 2010
Online version
Paper: [1]
Summary
This paper tries to identify communities in multi-mode networks (networks which contain heterogeneous types of actors and different interactions between these actors). Other goal of this paper is to detect and identify the changes between community memberships and the interactions between actors during time. The authors used a temporally-regularized framework to solve this problem. They examined their framework on both synthetic and real datasets and obtained good results in efficiency and generality of their method.
Brief description of the method
Experimental result
Dataset used
Enron data
DBLP data
