Node-similarity-based network community division method in network

Abstract

The invention discloses a node-similarity-based network community division method in a network. The method comprises the following steps: extracting information of network community division preferences of users and determining weights of attribute; acquiring datasets from user profiles in the network; taking each node as a network community, calculating the similarity between different network communities, and making network community pairs generate a max heap according to the similarity; selecting node pairs with the maximum similarity and combining the node pairs; calculating the similarity between a new network community and the other network communities, inserting the new network community in the max heap, and deleting related records of the combined network communities from the max heap; calculating the Q values of new network community divisions; repeating the steps til all the nodes are combined into the same network community, and finding out the extreme points of the Q values and corresponding divisions; modifying the weights of attribute, and repeating the arithmetic; and collecting the extremes of the Q values at the evaluation of the different weights and corresponding network divisions, and recommending the divisions with higher Q values to the users.

Description

technical field [0001] The invention relates to the technical field of computer software, in particular to a network community division method based on node similarity in the network, which realizes the network community division of nodes in complex networks by analyzing the attributes of nodes and links in the network. Background technique [0002] Network structures widely exist in nature and real life, such as communication networks, transportation networks, power networks, aviation networks, food chain networks, and protein interaction networks. Although the above network structures come from different fields and have different backgrounds, they have similar structures. Features, such as "Power-Law", "Small-World", "High Clustering Coefficient", "Self-Similarity" of the network; At the same time, the network connection structure may change over time, and connections may have different weights or directions. In the real society, people are not isolated. Different social ...

AI Technical Summary

Problems solved by technology

[0005] However, we found that the existing research methods used the topology of the network to explore the network community in the network, and did not make comprehensive use of the node information in the network, which may result in inaccurate division of the network community structure and fail to achieve The ideal requirements of users; at the same time, with the continuous deepening of research on network community structure detection, users' demands on network community structure tend to be diversified, and different users have different starting points or preferences for network community structure detection. Therefore, in the past, only The research method that provides users with a single segmentation result is gradually unable to meet the needs of users

Images