Dynamic heterogeneous network node classification method based on meta-graph extensible representation

Abstract

The invention belongs to the field of data analysis, and discloses a metagraph extensible representation-based dynamic heterogeneous network node classification method, which comprises the following steps of: obtaining scientific cooperation dynamic heterogeneous information network data including network nodes and network edge data; an embedding mechanism in a complex space is introduced to represent a given dynamic heterogeneous information network at a timestamp 1; learning a dynamic heterogeneous information network from a timestamp 2 to a timestamp t by adopting a ternary graph dynamic embedding mechanism; processing a heterogeneous information network from a timestamp 1 to a timestamp t by using a deep automatic encoder based on a long short-term memory network, and performing graph prediction of a timestamp t + 1 after analysis and calculation; and classifying the nodes in the network by using the graph data from 1 to t + 1 to obtain a classification result. According to the method, training is carried out on a change data set based on a meta-graph mechanism, a large-scale dynamic heterogeneous information network can be expanded, and a future network structure can be predicted.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a dynamic heterogeneous network node classification method based on meta-graph scalable representation. Background technique [0002] Content representation is a fundamental task in information retrieval. The purpose of representation learning is to capture the features of informative objects in a low-dimensional space. Most studies on representation learning for Heterogeneous Information Networks (HINs) focus on static HINs. In reality, however, the web is dynamic and constantly changing. A Heterogeneous Information Network (HIN) is an evolving network with multiple types of nodes and edges. In fact, most networks are dynamic heterogeneous information networks, such as social networks and bibliographic networks. Thus, compared to static networks, dynamic heterogeneous information networks are a more expressive tool for modeling information-rich pr...

AI Technical Summary

Problems solved by technology

This makes most existing static embedding models that need to process the entire network step by step, inappropriate and inefficient

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