Community discovery method of complex network and important node discovery method of community

Abstract

The invention discloses a community discovery method of a complex network and an important node discovery method of a community. The community discovery method of the complex network comprises the following steps: firstly, according to a topological structure of the complex network and connection frequencies among nodes, calculating to obtain the edge weight of each edge; taking the edge with the maximum edge weight as a starting edge of a new community to expand a new community; calculating a local communication judgment function of a community neighbouring point when expansion is carried out each time; dividing the neighbouring point and a corresponding edge into the community if the local communication judgment function is greater than 0; and after the community stops growing, selecting the edge with the maximum edge weight from the residual edges as the starting edge of another new community to continue to divide. When the important node discovery of the community is carried out, an influence factor, which is affected by the adjacent point of the same community, of each node is firstly calculated, and then, the importance degree of the node is obtained according to the influence factor, which is affected by the adjacent point, of each node and the influence factor, which is affected by the node, of the adjacent point to finish important node discovery. The community discovery method and the important node discovery method are high in accuracy and low in time complexity and are especially suitable to apply to the large-scale complex network.

Description

technical field [0001] The invention belongs to the technical field of complex networks, and more specifically relates to a method for discovering communities in complex networks and a method for discovering important nodes in communities. Background technique [0002] The research of complex network is a hot spot in the current scientific research, it has penetrated into various fields, and has become an important research direction in these fields. Complex networks in different fields can be represented in the form of graphs. Points represent individuals in the complex network, edges represent the relationship between individuals in the complex network, and the weight of the edge is used to represent the connection between individuals. It can be seen from this that the structure of a complex network is composed of a large number of points and the interaction relationship between them. With the in-depth study of complex networks, it is found that there are a large number ...

AI Technical Summary

Problems solved by technology

Due to the high time complexity of the classic community discovery algorithm, it has been unable to complete the community discovery of large-scale complex networks. It is imminent to study community discovery algorithms for large-scale complex networks with high accuracy and low time complexity.

The classic community discovery algorithm takes nodes as the division object, and obtains the community structure of points by designing an appropriate division method for nodes, but the community discovery algorithm that takes nodes as the division object needs to find the key nodes in the network first, from the key nodes Start to discover communities in the network, which leads to the accuracy of these algorithms also being related to the key nodes in the network

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