Social network key member detection method based on sparse evolution algorithm

Abstract

The invention discloses a social network key member detection method based on a sparse evolution algorithm, and the method is characterized in that the method comprises the following steps: 1, constructing a target function; 2, scores of all members in the social network are calculated, and a state population is initialized; 3, taking the state in the state population as a parent according to the designed genetic operator, generating a new state, and adding the new state into a filial generation state population; and step 4, merging the filial generation state population with the previous generation state population, performing operations of deleting repeated states, performing non-dominated sorting, calculating crowding distances among members and the like, and then selecting states of which the number is the same as that of the original state population as a new state population in an inverted sequence until a group of social networks consisting of key members and non-key members is obtained. The time for identifying the key members in the large complex social network can be shortened, and the accuracy for identifying the key members is improved.

Description

technical field [0001] The invention belongs to the field of detecting key members of a social network, in particular to a method for detecting a key member of a social network based on a sparse evolutionary algorithm. Background technique [0002] A social network is a social structure composed of nodes such as many individuals or organizations, representing various social relationships. Social networks have four characteristics: swiftness, spreading, equality and self-organization. Because of these characteristics, this network has an impact on all aspects of the real society. From the perspective of member classification, identifying members with high reputation and influence in social networks is very important when designing marketing strategies. Positioning a product can have a huge impact on society if the strategy is adapted to the most influential and recognized members of the local community. However, if some information such as public opinion also takes advanta...

AI Technical Summary

Problems solved by technology

However, the number of key members in a huge social network is relatively small, that is to say, the key members are sparse, and the current evolutionary algorithm does not take this into account, resulting in an increase in the time consumed for identifying key members, and identifying will also be less accurate

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