A Prevention Strategy Against Rumor Spreading in Complex Networks, M.Sc. Thesis Sharif University of Technology ; Khansari, Mohammad (Supervisor)
Abstract
Complex networks have recently become a very noticeable subject because of their remarkable theory on real networks such as rumor spreading. This thesis contributes to a prevention strategy which uses a maximal clique algorithm to find overlapping communities and also uses inverse targeting immunization strategy to immune these communities. By the using maximal clique algorithm combined with inverse targeting strategy we have achieved that the TIK method (targeted immunization using k-clique percolation) obtains 10% of better immunization than HD (highest degree) method which is one of the best methods in targeted immunization strategies. The TIK also demonstrates that rumor spreading is...
Cataloging briefA Prevention Strategy Against Rumor Spreading in Complex Networks, M.Sc. Thesis Sharif University of Technology ; Khansari, Mohammad (Supervisor)
Abstract
Complex networks have recently become a very noticeable subject because of their remarkable theory on real networks such as rumor spreading. This thesis contributes to a prevention strategy which uses a maximal clique algorithm to find overlapping communities and also uses inverse targeting immunization strategy to immune these communities. By the using maximal clique algorithm combined with inverse targeting strategy we have achieved that the TIK method (targeted immunization using k-clique percolation) obtains 10% of better immunization than HD (highest degree) method which is one of the best methods in targeted immunization strategies. The TIK also demonstrates that rumor spreading is...
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