Please use this identifier to cite or link to this item:
Multiscale community detection using markov dynamics
|Altunkaya_Ali_r.pdf||Version for non-UH users. Copying/Printing is not permitted||1.56 MB||Adobe PDF||View/Open|
|Altunkaya_Ali_uh.pdf||Version for UH users||1.62 MB||Adobe PDF||View/Open|
|Title:||Multiscale community detection using markov dynamics|
|Issue Date:||Aug 2014|
|Publisher:||[Honolulu] : [University of Hawaii at Manoa], [August 2014]|
|Abstract:||Complex networks is an interdisciplinary research area getting attention from a variety of disciplines including sociology, biology, and computer science. It studies the properties of complex systems that may have many functional or structural subunits. Community detection algorithms are one of the major approaches to analyse complex networks by finding these intermediate-level subunits called modules or communities. Furthermore, some networks may have multilevel or overlapping community structures.|
InfoMap is one of the best performing algorithms that finds the communities of a network by compressing the flow of information [25, 17]. In this work, we introduced Markov Dynamics to the InfoMap in order to detect communities at multiscales. Although Markov Dynamics have been applied to the InfoMap for undirected networks before , this was the first application of Markov Dynamics to the InfoMap for directed networks. Additionally, we added a feature to detect overlapping nodes using the compression of flow on the boundary nodes, similar to the approach described in  before.
These two features were combined into the InfoMap for directed networks. We evaluated these two features on synthetic networks and benchmark graphs. We have comparable results with the Hierarchical InfoMap  for multilevel community detection, but improvement in runtime is needed. We evaluated the overlapping feature by its own ability, and found that it can detect overlapping nodes for networks with sparse overlaps.
|Description:||M.S. University of Hawaii at Manoa 2014.|
Includes bibliographical references.
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||M.S. - Computer Science|
Please contact firstname.lastname@example.org if you need this content in an alternative format.
Items in ScholarSpace are protected by copyright, with all rights reserved, unless otherwise indicated.