Volume 90, Number 1, June 2010
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||27 April 2010|
Community detection with and without prior information
Yerevan Physics Institute - Alikhanian Brothers Street 2, Yerevan 375036, Armenia
2 Information Sciences Institute, University of Southern California - Marina del Rey, CA 90292, USA
Corresponding author: firstname.lastname@example.org
Accepted: 23 March 2010
We study the problem of graph partitioning, or clustering, in sparse networks with prior information about the clusters. Specifically, we assume that for a fraction ρ of the nodes their true cluster assignments are known in advance. This can be understood as a semi-supervised version of clustering, in contrast to unsupervised clustering where the only available information is the graph structure. In the unsupervised case, it is known that there is a threshold of the inter-cluster connectivity beyond which clusters cannot be detected. Here we study the impact of the prior information on the detection threshold, and show that even minute (but generic) values of ρ > 0 shift the threshold downwards to its lowest possible value. For weighted graphs we show that a small semi-supervising can be used for a non-trivial definition of communities.
PACS: 89.75.Fb – Structures and organization in complex systems / 89.75.Hc – Networks and genealogical trees / 02.70.Rr – General statistical methods
© EPLA, 2010
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.