0000000000050116

AUTHOR

Günce Keziban Orman

0000-0003-0402-8417

showing 3 related works from this author

Qualitative Comparison of Community Detection Algorithms

2011

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis r…

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciences02 engineering and technologyPhysics and Society (physics.soc-ph)Similarity measure[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Complex NetworksField (computer science)Qualitative analysis020204 information systems0202 electrical engineering electronic engineering information engineeringSocial and Information Networks (cs.SI)Algorithms ComparisonArtificial networks[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science - Social and Information Networks[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Complex networkPartition (database)Community Properties020201 artificial intelligence & image processingAlgorithmCommunity Detection
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Towards realistic artificial benchmark for community detection algorithms evaluation

2013

Many algorithms have been proposed for revealing the community structure in complex networks. Tests under a wide range of realistic conditions must be performed in order to select the most appropriate for a particular application. Artificially generated networks are often used for this purpose. The most realistic generative method to date has been proposed by Lancichinetti, Fortunato and Radicchi (LFR). However, it does not produce networks with some typical features of real-world networks. To overcome this drawback, we investigate two alternative modifications of this algorithm. Experimental results show that in both cases, centralisation and degree correlation values of generated networks…

FOS: Computer and information sciencesPhysics - Physics and Societypreferential attachmentComputer Networks and CommunicationsComputer science[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]FOS: Physical sciencesvirtual communitiesPhysics and Society (physics.soc-ph)01 natural sciences010305 fluids & plasmasEducation0103 physical sciencescommunity detectionbenchmarking010306 general physicsSocial and Information Networks (cs.SI)CommunicationComputer Science - Social and Information Networkscomplex networksweb based communitiesonline communitiesconfiguration modellingIdentification (information)LFR benchmarkBenchmark (computing)[ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH]community structureAlgorithmtopological propertiesSoftware
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Comparative Evaluation of Community Detection Algorithms: A Topological Approach

2012

International audience; Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups. Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.). However, this type of comparison neglects the topological properties of the communities. In this article, we present a comprehensive comparative study of a representative set of commu…

Statistics and ProbabilityFOS: Computer and information sciencesPhysics - Physics and SocietyComputer science[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]Rand indexFOS: Physical sciences02 engineering and technologyPhysics and Society (physics.soc-ph)Topology01 natural sciencesMeasure (mathematics)010305 fluids & plasmasSet (abstract data type)Development (topology)0103 physical sciences0202 electrical engineering electronic engineering information engineeringEquivalence (measure theory)Random graphSocial and Information Networks (cs.SI)Computer Science - Social and Information NetworksStatistical and Nonlinear PhysicsNetwork dynamicsPartition (database)[ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH]020201 artificial intelligence & image processingStatistics Probability and Uncertainty
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