Large graph sampling visualization method based on graph representation learning

Abstract

The invention discloses a large graph sampling visualization method based on graph representation learning, and belongs to the field of graph visualization and graph sampling. According to the method,nodes in an original network are converted into high-dimensional vectors through a node2vec algorithm, then the high-dimensional vectors of the nodes are projected to a low-dimensional space througha dimension reduction algorithm, and the semantic structure similarity of the corresponding nodes in the network space can be effectively expressed through the distance between projection points. Secondly, a multi-target sampling model of adaptive blue noise sampling is designed to effectively maintain a topological structure of an original network; and measurement indexes based on network attribute characteristics are proposed, quantitative evaluation is carried out on different sampling algorithms to obtain graph sampling result evaluation, and the graph sampling result evaluation is presented by utilizing a visualization method. According to the method, the nodes are sampled in the representation space, the context structure of the original network is well simplified and reserved, and the topological structure of the network is effectively kept while the node scale is reduced.

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...

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.

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