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Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-25 05:51:24 | 只看該作者
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發(fā)表于 2025-3-25 09:37:32 | 只看該作者
,Subgraph Federated Learning with?Global Graph Reconstruction, Existing cutting-edge methods typically allow clients to exchange data with all other clients to predict missing neighbor nodes. However, such client-to-client data exchanges are highly complex and lead to expensive communication overhead. In this paper, we propose FedGGR: subgraph federated learni
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發(fā)表于 2025-3-25 12:39:34 | 只看該作者
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發(fā)表于 2025-3-25 16:53:27 | 只看該作者
SEGCN: Structural Enhancement Graph Clustering Network,nging research task for graph-structured data. In recent years, existing graph clustering methods have gotten better performance without human guidance by combining auto-encoder and graph convolution networks. However, the existing methods exist problems: 1) the aggregation of noise information in t
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發(fā)表于 2025-3-25 21:52:42 | 只看該作者
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發(fā)表于 2025-3-26 19:49:49 | 只看該作者
,Joint Training Graph Neural Network for?the?Bidding Project Title Short Text Classification, in the face of complex and complicated bidding information, how to select the bidding projects that meet their needs and process them in a shorter time, the classification of project titles has become an urgent problem to be solved. Considering the characteristics of the short text of bidding title
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