6523

6085

كارشناسي ارشد

ماده چگال

اصفهان: دانشگاه صنعتي اصفهان، دانشكده فيزيك

1390

هشت، 72ص.: مصور، نمودار

ص.ع. به فارسي و انگليسي

كيوان آقا بابايي ساماني

مجتبي اعلايي

17/12/90

فرهاد شهبازي، فرهاد فضيله

1396/10/12

كتابنامه

فيزيك

فيزيك

ID6085

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Detecting Community Structures in Complex Networks Using Clumpiness Matrix Ali Faqeeh faqeeh ali@gmail com Date 09 11 2011 Supervisor Dr Keivan Aghababaei Samani samani@cc iut ac ir Department of Physics Isfahan University of Technology Isfahan 84156 83111 IranDegree M Sc Language PersianAbstract The use of complex networks as efficient models for describing a wide variety of natural systemsand phenomena is increasingly extending For investigating a network perceiving its structure isextremely important and is also necessary for understanding its function Communities are of themost significant structural concepts In communities there are plenty of and often strong relationsbetween the members of the same community in contrast to sparse connection between members ofdifferent modules This causes that the communities show some different behavior rather than that ofthe overall network while affecting the function of the network too Up until now many researchersin various fields have workd on this subject which has leaded to enormous range of communitydetection methods with deferent basics and perspectives However the community detection problemhas not been satisfactorily and comprehensively solved yet and it is regarded as an open questionin complex networks Of popular approaches to this problem are optimization methods which arebased on maximizing modularity or another suitable quality function dynamical algorithms whichemploy mobile processes in the network and spectral methods which use a network matrix in orderto find community structures In this text finding communities based on the clumpiness matrix incomplex networks is investigated and analyzed In this method eigenvectors of clumpiness matrixare used to construct a projection space In such a space the accumulation of points in branches which correspond to communities is observed These branches are divided by defining a borderlineand or using hierarchical clustering methods yielding the communities of the network A physicaljustification based on the interactional relation between nodes and considering the clumpiness matrixas the Hamiltonian of the system is presented to explain the performance of the method Accordingly the effect of heterogeneity in the community size distribution is discussed Then the computationalresults of the method are presented and its performance on benchmark and real networks is comparedwith other algorithms Keywords Real networks community structure random graphs quality function spectral analysis dynamical processes

كيوان آقا بابايي ساماني

مجتبي اعلايي

فرهاد شهبازي، فرهاد فضيله