Unsupervised graph representation learning method and device on large-scale attribute graph based on sub-graph sampling

Abstract

The invention relates to an unsupervised graph representation learning method and device on a large-scale attribute graph based on subgraph sampling. The method comprises the steps of performing sub-graph sampling on an attribute graph according to structure information and node attribute information of the attribute graph to generate a plurality of sub-graphs; and performing graph learning on anauto-encoder on each sub-graph by utilizing the structure information, the node attribute information and the community information of the attribute graph to obtain low-dimensional vector representation of nodes in the attribute graph. The graph auto-encoder comprises an encoder and a decoder; the encoder adopts a graph convolutional neural network; the decoder includes a graph structure loss reconstruction decoder, a graph content loss reconstruction decoder, and a graph community loss reconstruction decoder. A user is supported to learn low-dimensional vector representations of nodes in a large-scale attribute graph in an unsupervised mode, topological structure information and node attribute information on the graph can be reserved as much as possible through the vector representations,and the vectors serve as input to be applied to different downstream tasks to conduct data mining tasks on the graph.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to an unsupervised graph representation learning method and device on a large-scale attribute graph based on subgraph sampling. Background technique [0002] In recent years, with the rapid development of the Internet, the scale of data has exploded, and the connection between data has become more and more complex. Graphs describe things and the relationship between things in the form of points and edges. It is a data structure that can intuitively describe the objective world. It exists widely in production and life, such as social networks, traffic road networks, and e-commerce. The nodes in these graphs usually have rich attribute information. For example, in the paper citation network, nodes represent papers, edges represent citation relationships, and the attribute information on nodes is the content of abstracts or full texts of papers. How to efficiently min...

AI Technical Summary

Problems solved by technology

The algorithm based on the graph neural network can simultaneously utilize the structural information and node attribute information in the graph, but it is only applicable to small graphs (thousands of nodes, tens of thousands of edges), and the scalability is limited.

[0006] From the perspective of information utilization, the existing algorithms, that is, the above two types of algorithms, only focus on the local structural information in the graph in the loss function, and reconstruct edges or local neighbor nodes, lacking information on node attributes and high-level information on the graph. Utilization of hierarchical structure information (such as community)

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