Method for network visualization

Abstract

The invention discloses a method for the network visualization, which belongs to the field of network technology. The method comprises the following steps of: using a buffer read-in mechanism of Java to read large network data and store the large network data into an internal memory; performing hierarchical division of the network by a community dividing method; putting the circle centre of the community of the largest hierarchy in the centre of a visual image according the principle that the radius of each community is equal to the product of the number of the initial nodes contained therein and the radius of the initial node so as to locate the community of each hierarchy by the circular layout which has a hierarchical inclusion structure and draw graphics; providing mouse response on the network graphics, so the user can switch different hierarchies and view the over-all and local structures of the network. In the invention, the modularity index-based community dividing method is adopted, so the physical meaning of the community is clear; the divided community has a multi-level recursive structure, so stacking of a great number of nodes and crossing of edges are avoided and the structure of the network can be viewed more clearly.

Description

technical field [0001] The invention relates to a method in the field of network technology, in particular to a network visualization method. Background technique [0002] In recent years, the research on complex networks has gradually emerged. From the Internet to global transportation networks, from brains in living organisms to various metabolic networks, from scientific research cooperation networks to various social relationship networks, it can be said that human beings live in a world full of various networks. For any network, we can regard it as a system composed of some nodes connected together in a certain way. Network visualization technology can not only realize rapid information sharing, but also use the information in the graph to assist decision-making, which is of great help to network analysis. However, with the continuous expansion of network scale and increasingly complex structure, large-scale network visualization research is also facing more and more ...

AI Technical Summary

Problems solved by technology

However, this technology has the following two disadvantages: the cluster division is a custom function, and the specific physical meaning is not clearly defined; the cluster division has only one layer, so the number of nodes contained in each cluster is still large, and the drawing will be difficult. The accumulation of a large number of nodes and the intersection of edges make it difficult for people to distinguish with the naked eye

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