Community discovery method based on local optimization

Abstract

The invention relates to the community discovery field in a complex network, and specifically discloses a community discovery method based on local optimization. On the basis of an LFM (Local FitnessMaximum) algorithm, on the one hand, a weighting method for integrating internodal boundary social attribute with common neighbor node degree in the network is proposed, and a fitness function in theLFM algorithm is updated by using the formed boundary weight; on the other hand, the concept about the local community stability is imported into the local optimization process of the LFM algorithm, the contribution degree on the community stability by the node is judged by computing the community stability before and after the node adds in the local community, and the contribution degree is usedas the criterion whether the node is added in the community. Through the method disclosed by the invention, the appearance of the over-sized community is avoided, the more significant groupuscule structure in the network can be easily discovered, and the method is suitable for the real social network.

Description

technical field [0001] The invention relates to a community discovery method based on local optimization, which belongs to the field of complex network and community mining. Background technique [0002] A social network is a social structure composed of many nodes according to a certain relationship. A node usually refers to an individual or an organization. A certain relationship here can be various, such as cooperative relationship, social relationship, communication relationship, etc. With the rapid development of social networks and communication networks, the scale of nodes in the network is getting larger and larger, and the relationship between nodes is becoming more and more complex. According to Facebook CEO Mark Zuckerberg, as of the second quarter of 2017, the number of monthly active users of Facebook exceeded 2 billion for the first time, an increase of 17% compared with the same period last year. Since June 9, there have been 17 million Facebook users. People...

AI Technical Summary

Problems solved by technology

However, the algorithm ignores the weight information of the links between nodes, especially in social networks, where the connection between people is very close, and the fitness calculated by relying solely on node degree information cannot fully reflect the characteristics of social networks

On the other hand, the LFM algorithm selects the node that contributes the most to the local community fitness value to join the community each time. The access conditions of this node may be too simple, which will cause the emergence of a super large community, thus affecting the effect of community discovery

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