Incremental social event detection method of graph neural network

Abstract

The invention discloses an incremental social event detection method based on a graph neural network, and the method comprises the steps: extracting information in a text for the social network data transmitted in a streaming manner, and carrying out the heterogeneous information network modeling; obtaining an isomorphic network; obtaining an isomorphic social contact message graph; learning the isomorphic message graph by adopting a graph attention model to obtain a message code based on knowledge retention increment; meanwhile, sampling message codes, carrying out comparative learning to calculate loss, adjusting parameters of the graph attention model according to the returned loss, and training the graph attention model; and performing clustering on codes obtained by detecting the graph attention model to obtain social events. Rich semantic and structural information is fully fused into the social messages, knowledge obtained from the social messages is reserved through the graph neural network, the comparative learning technology is adopted, the message graph is periodically maintained, and the event detection accuracy can be improved under the condition that excessive resources are not consumed.

Description

technical field

[0001] The invention belongs to the technical field of social network event detection, and in particular relates to a graph neural network incremental social event detection method. Background technique

[0002] With the development of Internet technology, global informatization data is showing the characteristics of explosive growth, massive agglomeration, and rapid dissemination. Human society has entered the "big data era", which has had a significant impact on cultural dissemination, social governance, etc., from massive data The technology of detecting social events in China has attracted more and more attention and has become a hot topic of the moment. Event detection refers to the technology of mining events that occur in real society by analyzing social network data. Compared to traditional text, social messages are generated and streamed chronologically; the content is short and often contains acronyms not found in dictionaries; contains various typ...

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