Graph layout method and device for large-scale network

Abstract

The invention discloses a graph layout method for a large-scale network, and the method comprises the steps of enabling each node in graph data to be expressed as a low-dimensional dense vector through a network embedding expression model based on machine learning, and constructing an embedding matrix of the graph data; and projecting the embedded matrix through an improved nonlinear dimension reduction algorithm to obtain a graph layout result of graph data in a two-dimensional space. The invention further discloses a graph layout device for the large-scale network. According to the invention, the calculation efficiency is higher, the required storage space is smaller, the local and global structure characteristics of the graph data can be maintained, meanwhile, the nodes with higher degree values in the graph data can be relatively dispersed from the neighbor nodes under the condition of maintaining local structure information, and the possible congestion or overlapping phenomenon can be effectively relieved.

Description

technical field [0001] The invention belongs to the field of network data processing, and in particular relates to a large-scale network-oriented graph layout method and device. Background technique [0002] Facing the growing large-scale data, graph visualization has become an important network data analysis method, which plays an important role in many application fields, such as biomedical network, chemical molecular network, transportation network, financial transaction network, academic cooperation networks, social networks, etc. We know that graph visualization consists of three parts: graph layout, network attribute expression and reasonable user interaction, and the core element is graph layout. The expression of network attribute and reasonable user interaction are premised on a good graph layout, so graph visualization One of the major areas of research in this field is graph layout. [0003] Graph layout methods are mainly classified into two categories: graph l...

AI Technical Summary

Problems solved by technology

For graph data with a very large number of nodes, the space and time complexity of obtaining this matrix is ​​unacceptable

[0009] To sum up, the main problems of graph layout technology based on nonlinear dimensionality reduction are as follows: First, when calculating the similarity between nodes in graph data, it is necessary to use the shortest path distance of graph theory to construct the shortest path distance matrix of graph data ( Shortest-Path Distances Matrix, SPDM), the matrix size is |V|×|V|, and store the matrix for subsequent use

For large-scale network layouts, this time and space complexity is unbearable

Secondly, the existing graph layout technology based on nonlinear dimensionality reduction does not consider the centrality difference of different nodes when defining the objective function. Nodes with higher degrees have more edges. In a two-dimensional layout space, nodes possible overlap between

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