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A Large Graph Sampling Visualization Method Based on Graph Representation Learning

A chart and representation technology, applied in still image data browsing/visualization, still image data retrieval, special data processing applications, etc., can solve problems such as not considering network semantic structure association, difficult connection of sampling results, uncertainty, etc., to achieve Maintain the characteristics of the network structure, simplify the context structure, and achieve the effect of reducing the scale

Active Publication Date: 2021-11-05
ZHEJIANG UNIV OF FINANCE & ECONOMICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, the random sampling scheme based on nodes or edges can more evenly capture the aggregation characteristics of the original network, but does not consider the semantic structure association of the network, making it difficult to keep the sampling results connected, and the structural information of the original network may be seriously lost; while The random sampling scheme based on traversal further considers the correlation properties between nodes, and its sampling results can better maintain the connectivity of the network, but it is also easy to fall into local traps, and cannot well maintain the global structural characteristics of the original network. strong uncertainty
However, due to the strong coupling relationship between nodes, some topology-based sampling algorithms inevitably generate high-complexity calculation problems, making it difficult to process and analyze large-scale network graphs.

Method used

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  • A Large Graph Sampling Visualization Method Based on Graph Representation Learning
  • A Large Graph Sampling Visualization Method Based on Graph Representation Learning
  • A Large Graph Sampling Visualization Method Based on Graph Representation Learning

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Embodiment Construction

[0018] Below in conjunction with accompanying drawing, the present invention will be further elaborated.

[0019] like figure 1 It is a flow chart of the large image sampling visualization method of the present invention, which specifically includes the following steps:

[0020] Step 1): Build a corpus. First, simulate a random walk sequence of fixed length L from a given source node u, w i Indicates the i-th node in the sequence, w i-1 Represents the i-1th node in the sequence. from w i = start from u, node w i Generated as shown in formula (1):

[0021]

[0022] That is, if there is an edge (v, x) in the network graph E, then the probability Select the next node x. Among them, π vx is the unregularized transition probability from node v to x, and Z is the regularization constant.

[0023] Then, based on the idea of ​​2nd-order random walks, let π vx = α pq (t,x), as shown in formula (2):

[0024]

[0025] Among them, d tx Indicates the distance of the sho...

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Abstract

The invention discloses a large graph sampling visualization method based on graph representation learning, and belongs to the fields of graph visualization and graph sampling. The method uses the node2vec algorithm to convert the nodes in the original network into high-dimensional vectors, and then uses the dimensionality reduction algorithm to project the high-dimensional vectors of the nodes into a low-dimensional space, and the distance between the projected points can effectively express the corresponding nodes in the network space. Semantic structure similarity in . Then, a multi-objective sampling model of adaptive blue noise sampling is designed to effectively maintain the topology of the original network; a measurement index based on network attribute characteristics is proposed, and different sampling algorithms are quantified and evaluated, and the graph sampling result evaluation is obtained, which is presented by a visualization method Graph sampling result evaluation. The method samples the nodes in the representation space, well simplifies and preserves the context structure of the original network, and effectively maintains the topology structure of the network while reducing the scale of the nodes.

Description

technical field [0001] The invention belongs to the field of graph visualization and graph sampling, and in particular relates to a large graph sampling visualization method based on graph representation learning. Background technique [0002] Graph visualization technology can provide a comprehensive and multi-angle description for network graphs, allowing users to exploratoryly analyze the network structure and perceive hidden features in the network. However, when faced with large-scale network data, the visual exploration and analysis capabilities of the network are often severely affected by its large scale, for example, millions of nodes and edges overlap each other in a limited screen space, making it difficult for users to find interesting network association mode. [0003] Graph sampling technology aims to extract representative sample graphs from original large-scale network datasets, which is a common method to reduce data scale and improve user analysis efficien...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/54
CPCG06F16/54
Inventor 周志光石晨王浩轩邹嘉玲
Owner ZHEJIANG UNIV OF FINANCE & ECONOMICS
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