Extraction of community transition rules from social bookmark data as graph sequence

In this study, we use transaction data collected from a data stream regarded as a graph representing the change in structure and sequence data for each relevant time period to analyze the changes in the sequence graph of a community. The algorithm proposed in this paper uses the hierarchical cluster...

Full description

Saved in:
Bibliographic Details
Published in2011 IEEE International Conference on Systems, Man, and Cybernetics pp. 3572 - 3579
Main Authors Yamaguchi, T., Niimi, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2011
Subjects
Clustering
Communities
Community Transition Rules
Data mining
Data structures
Evolution (biology)
Graph Kernels
Graph Sequence
Kernel
Maintenance engineering
Periodic structures
Social Bookmark
Online AccessGet full text

Cover

More Information
Summary:In this study, we use transaction data collected from a data stream regarded as a graph representing the change in structure and sequence data for each relevant time period to analyze the changes in the sequence graph of a community. The algorithm proposed in this paper uses the hierarchical clustering method combined with a graph kernel extension to analyze the relationship among the chart series of the entire community. Extracted community rules appear occasionally, and then are shown to disappear in the middle of the series. The results of experiments using synthetic datasets and real social bookmark data show that changes in the community captured occasional occurrence of the proposed algorithm.
ISBN:9781457706523
1457706520
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2011.6084223