Software network key node mining method based on complex network

Abstract

The invention provides a software network key node mining method based on a complex network. The method comprises the steps of firstly according to a network topological structure, taking the number of the methods in a class as the weights of the edges, and redefining the concept of the edge weight value, namely weighting the edges in a directed network of a software system according to the numberof the methods of the class to abstract a directed weighted network model; and then using the nodes which are judged as the alternative key nodes by different key node mining algorithms as the finalkey nodes in the directed weighted network model, so that the obtained key nodes are the key nodes occupying more important positions in the software network, and by protecting the found key nodes ofthe software system, the reliability and the safety of the software system can be enhanced, the attacks from the outside on the software system are effectively reduced, and the loss caused by the damage to the system is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of software security, and in particular relates to a method for digging key nodes of a software network based on a complex network. Background technique [0002] Key nodes have great influence in software networks, and in real networks such as mobile Internet, scientific research and education networks, physical networks and even biological networks, key nodes play a vital role. With the in-depth study of complex networks in the relevant characteristics of software networks and the broad application prospects of key nodes, the research on key nodes is also playing an increasingly important role in software complex networks. In order to evaluate the influence of nodes, researchers will conduct research and analysis on all abstracted nodes and the relationship between nodes in the software network, quantify the status and influence of nodes in the entire network through certain conditions, and then dig out sof...

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Problems solved by technology

[0004] However, most of the current measurement methods are based on directed unweighted networks or undirected unweighted networks. On the basis of unweighted networks, parameter values ​​such as node degree, inter-node distance, aggregation coefficient, and correlation coefficient are extracted as measurement indicators, but no The weight network will ignore some important structural properties of the software system, making the measurement inaccurate

Moreover, such a network model is too simple to study. Although it has a good effect in representing the global characteristics of the software network, it has certain limitations in the local research on the software network.

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