A universal estimation formula for the average path length of scale free networks is given in this paper. Different from other estimation formulas, most of which use the size of network, N, as the only parameter, two parameters including N and a second parameter α are included in our formula. The parameter α is the power-law exponent, which represents the local connectivity property of a network. Because of this, the formula captures an important property that the local connectivity property at a microscopic level can determine the global connectivity of the whole network. The use of this new parameter distinguishes this approach from the other estimation formulas, and makes it a universal estimation formula, which can be applied to all types of scale-free networks. The conclusion is made that the small world feature is a derivative feature of a scale free network. If a network follows the power-law degree distribution, it must be a small world network. The power-law degree distribution property, while making the network economical, preserves the efficiency through this small world property when the network is scaled up. In other words, a real scale-free network is scaled at a relatively small cost and a relatively high efficiency, and that is the desirable result of self-organization optimization.
To accurately track computer viruses,an overlay network that monitors the activities of viruses is constructed.Identifying and locating nodes infected by virus on network is achieved by a naming system in which a node in the network is mapped to a unique serial number of the hard-drive.By carefully monitoring and recording sensitive communication between local system and remote nodes on the network,and suspicious operations on files that originate from remote nodes and entered via some form of file transfer,activities of viruses in both local and network level are recorded and ready for future analysis.These data can also be used in analysis of the mechanism of a computer virus as well as its spreading mode and pattern.
Li YingCao YiqunQiu BenJiao JianShan XiumingRen Yong
