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Large-scale diagram data representation method

A graph data, large-scale technology, applied in the field of large-scale graph data representation, can solve problems such as low query efficiency, reduce space consumption, improve storage efficiency, and improve efficiency.

Inactive Publication Date: 2017-05-31
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is that existing K 2 -tree has the problem of low query efficiency when representing large-scale graph data, and provides a large-scale graph data representation method

Method used

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Embodiment Construction

[0050] A large-scale graph data representation method, such as figure 1 shown, including the following steps:

[0051] Step 1. Guided by the idea of ​​density-based clustering, divide the graph data into clusters of different sizes.

[0052] Step 1.1. Abstract the graph data into an adjacency matrix representation.

[0053] Step 1.2. Use the elements in the first row and first column of the adjacency matrix as the starting point of the sub-matrix, expand the size of the sub-matrix along the main diagonal of the adjacency matrix, and increase the width of the sub-matrix by 1 each time it is expanded.

[0054] In step 1.3, after the expansion of the sub-matrix is ​​completed, the outlier rate φ and the size MinSize of the current sub-matrix are calculated.

[0055] The outlier rate φ of the above sub-matrix is: φ=m1 / m2; among them, m1 indicates that the row number belongs to the current sub-matrix, but the number of elements with a value of 1 whose column number has exceeded t...

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Abstract

The invention discloses a large-scale diagram data representation method which comprises the following steps: on the basis of K<2>-tree and clustering ideas, abstracting diagram data into an adjacency matrix representation mode by using a clustering idea based on density, further performing clustering processing on the basis of adjacency matrixes, therefore, including a colony structure with a great number of elements of which the values are 1 in a cluster. As a great number of elements of which the values are 1 are included in sub-matrixes, L vectors corresponding to K<2>-tree, of all sub-matrixes, are serially connected to form a global vector global-L, and DACs (Digital-to-Analog Converter) encoding is implemented, the space occupied by each element of which the value is 1 in the represented adjacency matrixes can be effectively reduced, that is, the storage efficiency can be improved. By adopting the large-scale diagram data representation method, diagram data with hundreds of millions of nodes and edges can be efficiently and compactly represented and compressed, in addition, direct and reverse neighbor checking operation on the nodes in the diagram data can be carried out.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a large-scale graph data representation method. Background technique [0002] Graph data refers to the use of graphs to abstractly represent data entities and their relationships. Data entities are represented as vertices in the graph, and relationships between data entities are represented as edges in the graph. Graph data is a very broad concept, and its main manifestations include web page graphs, social network graphs, biological information graphs, semantic Web, and knowledge graphs. Graph data is increasingly becoming an important processing object. [0003] In the context of big data, the scale of vertices of graph data often reaches tens of millions, while the number of edges reaches hundreds of millions. When using K 2 There are two main problems when -tree represents large-scale graph data: [0004] 1)K 2 -tree mechanically divides the adjacency matrix,...

Claims

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Application Information

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IPC IPC(8): G06T9/40
CPCG06T9/40
Inventor 常亮曾祥炫古天龙徐周波王荣
Owner GUILIN UNIV OF ELECTRONIC TECH
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