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