Method and system for carrying out community detection on symbol network based on dynamic evolution

Abstract

The invention relates to a symbol network community detection method on based on dynamic evolution, which overcomes the defects in the prior art that the detection efficiency is low and the complexity is high. The symbol network community detection method is realized by the following steps: (1) inputting an adjacent matrix; (2) calculating a similarity matrix; (3) determining a positive neighbor set of a node; (4) generating an initial state of the node; (5) updating the state of the node; (6) judging whether the state of the updated node is stable or not; (7) detecting a community structure of a symbol network and outputting a detection result. The method provided by the invention designs the similarity matrix by using a dynamic evolution kinetic equation aiming at the characteristics of the symbol network; the community detection of the symbol network can be realized based on a principle that the states in a community tend to be consistent. The efficiency that the node states tend to be consistent is effectively improved through a manner that the node states gradually get close to a positive neighbor average state along with the time, the complexity of symbol network community detection is reduced, and a community structure of the symbol network can be effectively and rapidly detected.

Description

technical field [0001] The present invention relates to the field of network technology, and more specifically, to a dynamic evolution-based symbolic network community detection method and system. The present invention can quickly and effectively detect each community of the symbolic network by using dynamic evolution technology and utilizing the principle that node states tend to be consistent. Background technique [0002] At present, the research on complex networks is penetrating into many different fields such as mathematics and physics, life sciences and engineering disciplines. The scientific understanding of the quantitative and qualitative characteristics of complex networks has become an extremely important and challenging topic in scientific research in the Internet age. Modern network science is an important approach to understanding complex systems. Many complex systems in the real world can be modeled as complex networks, such as information networks, social n...

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Problems solved by technology

However, the disadvantage of this method is that the final stable node phase of the symbolic network with positive connections between communities cannot be consistent, and the detection speed is slow.

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