A method for local community detection by finding maximal-degree nodes

Since obtaining complete information from large network is unrealistic nowadays, there is a growing emphasis on local community detection. However, some existing approaches are sensitive to the starting node's position, such as the communities discovered from nodes in boundary always have lower...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 8 - 13
Main Authors Qiong Chen, Ting-Ting Wu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Accuracy
Communities
Complex network
Dolphins
Educational institutions
Image edge detection
Local community detection
Measurement
Modularity
The maximal-degree node
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ISBN9781424465262
1424465265
ISSN2160-133X
DOI10.1109/ICMLC.2010.5581103

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Summary:Since obtaining complete information from large network is unrealistic nowadays, there is a growing emphasis on local community detection. However, some existing approaches are sensitive to the starting node's position, such as the communities discovered from nodes in boundary always have lower recall rate than those from nodes in the core. Thus, in this paper, we propose a new method to detect the local community for a given node. To start, we find the local maximal-degree nodes which associate with the given node, then find the enclosing communities by calculating the local modularity of community from the local maximal-degree node, finally we optimize the communities' structure and get the local community for the given node. Experiment results show that our method is quite effective and flexible, especially when the given node is in the community's boundary.
ISBN:9781424465262
1424465265
ISSN:2160-133X
DOI:10.1109/ICMLC.2010.5581103