Interactive community search method and device based on graph neural network
A neural network and community technology, applied in the information field, can solve problems such as restricting community search applications, ignoring content information, uncontrollable community size, etc., and achieve the effects of reducing user burden, accurate size, and high accuracy
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[0049] In order to make the purpose and technical solution of the present invention clearer, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0050] The present invention proposes an interactive community search method based on a graph neural network (GNN) in a social network, figure 1 A flowchart of the overall method. Given a query node q, construct a candidate subgraph containing q, train a GNN model to infer the probability of nodes in the community, and locate the target community in the subgraph. This process is repeated as user feedback is incorporated. The method is able to achieve higher effectiveness and efficiency while reducing the labeling burden for humans. Our data model is recorded as a graph G = (V, E, F, P), where V represents the node set, E represents the edge set, F represents the node content characteristics, and P represents the node score. For a node v∈V, F[v] is the content c...
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