Force-oriented graph layout method based on community discovery and clustering optimization

A technology of community discovery and force-directed graphs, applied in other database clustering/classification, network data navigation, structured data retrieval, etc., can solve the problems of lack of information analysis and mining, influence graph data understanding and judgment, insufficient display, etc. problem, to achieve the effects of easy understanding, optimized layout effect, and reduced calculation amount

Pending Publication Date: 2022-04-15
NORTHEASTERN UNIV
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Problems solved by technology

However, these optimization methods have the following problems: 1. Usually only adjustments are made for the mechanical model, and the geometric distance of the nodes in the layout formed by the mechanical model often has a certain error with the path length between nodes in the graph data, which affects the ac

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  • Force-oriented graph layout method based on community discovery and clustering optimization
  • Force-oriented graph layout method based on community discovery and clustering optimization
  • Force-oriented graph layout method based on community discovery and clustering optimization

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Embodiment Construction

[0055] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Preferred embodiments of the application are shown in the accompanying drawings. However, the present application can be embodied in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the application more thorough and comprehensive.

[0056] figure 1 It is a schematic flowchart of the force-directed graph layout method based on community discovery and clustering optimization in the present invention. Such as figure 1 As shown, the force-directed graph layout method based on community discovery and clustering optimization includes the following steps:

[0057] Step 1: Transform the raw data that needs to be analyzed using visual layout into corresponding graph data;

[0058] In this embo...

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Abstract

The invention discloses a force-oriented graph layout method based on community discovery and clustering optimization, and relates to the technical field of visual layout of graph data. Comprising the following steps: converting original data into corresponding graph data; dividing nodes of the graph data into leaf nodes and non-leaf nodes, regarding each non-leaf node as a community, and compressing the leaf nodes to obtain compressed graph data; performing a community discovery process of maximizing modularity in a first stage of a traditional Louvain algorithm on the compressed graph data; replacing the iterative community merging process in the second stage of the traditional Louvain algorithm with selective community merging for the updated graph data in the previous step; and for the community structure obtained in the step 4 and the corresponding graph data, realizing the force-oriented graph layout based on clustering optimization by using a CombboForce layout algorithm. According to the method, the layout efficiency of the force-oriented graph layout during visual layout of the graph data is improved, and the layout effect of the force-oriented graph layout during visual layout of the graph data is optimized.

Description

technical field [0001] The invention relates to the technical field of visual layout of graph data, in particular to a force-directed graph layout method based on community discovery and clustering optimization. Background technique [0002] Visual layout can convert data into graphics or images and display them on the screen and provide interaction, so as to visually display effective and valuable information in the data, which will play an important role in data analysis and mining. For different types of data, the visual layout needs to convert it into different types of graphics, thus forming different types of charts. [0003] Graph data is a kind of data that embodies complex data entities and data relationships, and exists in social networks, biological networks, mobile device Unicom networks, financial transaction networks and other fields. The data model of graph data can be regarded as a collection of nodes and edges, and can usually be expressed as the following ...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/906G06F16/954G06F16/28
Inventor 高天寒韩林珊
Owner NORTHEASTERN UNIV
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