In order to explore the topological changes of Normal Aging human Brain Functional Network (NABFN), a network evolution Model based on Naive Bayes (NBM) was proposed. Firstly, the probability of existing edges between nodes was defined based on link prediction algorithm of Naive Bayes (NB) and anatomical distance. Secondly, based on the brain functional networks of young people, a specific network evolution algorithm was used to obtain a simulation network of the corresponding middle-aged and old-aged gradually by constantly adding edges. Finally, a network Similarity Index (SI) was proposed to evaluate the similarity degree between the simulation network and the real network. In the comparison experiments with network evolution Model based on Common Neighbor (CNM), the SI values between the simulation networks constructed by NBM and the real networks (4.479 4, 3.402 1) are higher than those of CNM (4.100 4, 3.013 2). Moreover, the SI value of both simulation networks are significantly higher than those of simulation networks derived from random network evolution algorithm (1.892 0, 1.591 2). The experimental results confirm that NBM can predict the topological changing process of NABFN more accurately.

In order to investigate the cascading invulnerability attack strategy of complex network via community detection, the initial load of the node was defined by the betweenness of the node and its neighbors, this defining method comprehensively considered the information of the nodes, and the load on the broken nodes were redistributed to its neighbors according to the local preferential probability. When the network being intentionally attacked based on community detection, the couple strength, the invulnerability of Watts-Strogatz (WS) network, Barabási-Albert (BA) network, Erds-Rényi (ER) network and World-Local (WL) network, as well as network with overlapping and non-overlapping community under differet attack strategies were studied. The results show that the network's cascading invulnerability is negatively related with couple strength; as to different types of networks, under the premise that fast division algorithm correctly detects community structure, the networks invulnerability is lowest when the node with largest betweenness was attacked; after detecting overlapping community using the Clique Percolation Method (CPM), the network invulnerability is lowest when the overlapping node with largest betweenness was attacked. It comes to conclusion that the network will be largest destoryed when using the attack strategy of complex network via community detection.

In order to investigate the effects of community structure on cascading invulnerability, in the frame of a community structure network, the initial load of the node was defined by its betweenness, and the load on the broken node was redistributed to its neighboring nodes according to the preferential probability. When the node with the largest load being intentionally attacked in the network, the relation of load exponent, coupling-strength in a community, coupling-strength between communities, modularity function and the network's invulnerability were studied. The results show that the network's cascading invulnerability is positively related with coupling-strength in a community, coupling-strength between communities and modularity function, negatively related with load exponent. With comparison to BA (Barabási-Albert) scale-free network and WS (Watts-Strogatz) small-world networks, the result indicates that community structure lowers the network's cascading invulnerability, thus the more homogeneous betweenness distribution is, the stronger network's cascading invulnerability is.