复旦大学：Trapping in scale-free networks with hierarchical organization of modularity

第六届全国复杂网络会议CCCN2010 Trapping in scale-free networks with hierarchical organization of modularity 报告人:林苑 指导老师:章忠志副教授 复旦大学 2010.10.17

Trapping in scale-free networks with hierarchical organization of modularity 报告人： 林 苑 指导老师：章忠志 副教授 复旦大学 2010.10.17 第六届全国复杂网络会议 CCCN2010 20:19:48

Outline k Introduction about random walks k Concepts Applications k Our works Fixed-trap problem Multi-trap problem w Hamiltonian walks k Self-avoid walks

 Introduction about random walks  Concepts  Applications  Our works  Fixed-trap problem  Multi-trap problem  Hamiltonian walks  Self-avoid walks Outline 20:19:48

Random walks At any node, go to one of the neighbors of the node with equal probability

 At any node, go to one of the neighbors of the node with equal probability. Random walks - 20:19:48

Random walks At any node, go to one of the neighbors of the node with equal probability

Random walks -  At any node, go to one of the neighbors of the node with equal probability. 20:19:48

Random walks At any node, go to one of the neighbors of the node with equal probability

Random walks -  At any node, go to one of the neighbors of the node with equal probability. 20:19:48

Random walks At any node, go to one of the neighbors of the node with equal probability

Random walks -  At any node, go to one of the neighbors of the node with equal probability. 20:19:48

Random walks At any node, go to one of the neighbors of the node with equal probability

Random walks -  At any node, go to one of the neighbors of the node with equal probability. 20:19:48

Random walks Random walks can be depicted accurately by Markov Chain

 Random walks can be depicted accurately by Markov Chain. Random walks 20:19:48

Generic Approach k Markov chain k Laplacian matrix Generating Function

 Markov Chain  Laplacian matrix  Generating Function Generic Approach 20:19:48

Measures Mean transit time Ti k Mean return time k Mean commute time C 可=7+方

 Mean transit time Tij  Tij ≠ Tji  Mean return time Tii  Mean commute time Cij  Cij =Tij+Tji Measures 20:19:48