Network key point analysis method based on single-layer information flow transmission

Abstract

The invention discloses a network key point analysis method based on single-layer information flow transmission, and the method comprises the steps: obtaining network data based on a graph structure,and constructing a single-layer information flow transmission network according to the network data; preprocessing the single-layer information flow transmission network, and establishing an optimization objective function based on a network attacker and a network operator; constructing a game model according to the optimization objective functions of the network attacker and the network operator;establishing a single-layer network information flow blocking model from the perspective of the network attacker, and blocking nodes are determined as network key points. According to the invention,two concepts of a network attacker and a network operator are introduced to describe the problem of network security from different perspectives and construct a game model; and meanwhile, the targetsof the network attacker and the network operator are clearly described and are classified into the same model, so that a single-layer network information flow blocking model is established, and key nodes of the network are discovered while a blocking scheme is solved from the perspective of the attacker.

Description

technical field [0001] The invention relates to the technical field of complex networks, in particular to a network key point analysis method based on single-layer information flow transmission. Background technique [0002] Computer network functions mainly include realizing resource sharing, realizing rapid transmission of data information, improving reliability, providing load balancing and distributed processing capabilities, centralized management and comprehensive information services; the above functions can be abstractly summarized as: information flow transmission, key network nodes Or node clusters for information processing and network performance optimization. [0003] The key nodes of the network refer to some special nodes that can affect the structure or performance of the network to a greater extent than other nodes in the network. The failure of some nodes in the network may cause small-scale failure or load redistribution, which has little impact on the ov...

AI Technical Summary

Problems solved by technology

[0004] So far, a large number of methods for evaluating key nodes of the network have been proposed, such as degree centrality, betweenness centrality, proximity centrality, semi-local centrality, etc., but all have their own limitations.

Among them, 1. Degree centrality is a relatively direct indicator, but usually it is not very important or correct. Similar indicators include LocalRank, proximity and H index, etc.; 2. Betweenness centrality and proximity centrality Both are path-based centrality, which is a global metric and can more effectively identify key nodes in the network, but their computational complexity is higher; 3. Semi-local centrality aims to ignore the topological relationship between neighbors and Considering only the number of nearest neighbors and second neighbors of a node is a trade-off between relevance and computational complexity, but the position of a node in the network may play a more important role than global properties such as degree

Also, other ranking methods like Proximity, PageRank, LeaderRank, ClusterRank, etc. have similar limitations

[0005] In short, the existing network node importance ranking methods are all based on the above indicators, and focus on how to improve them. For different structures and types of networks, different indicators have their own advantages and limitations, especially The strengths and limitations of these metrics may vary more for interdependent and interconnected networks

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