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Titlebook: Big Data; 6th CCF Conference, Zongben Xu,Xinbo Gao,Jiajun Bu Conference proceedings 2018 Springer Nature Singapore Pte Ltd. 2018 artificia

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31#
發(fā)表于 2025-3-26 22:44:14 | 只看該作者
Multiple Meta Paths Combined for Vertex Embedding in Heterogeneous Networks vertices and relations, so it is difficult to deal directly with data mining. At present, although many state-of-the-art methods of network representation learning have been developed, these methods can only deal with homogeneous networks or lose information when handling heterogeneous networks. In
32#
發(fā)表于 2025-3-27 05:07:08 | 只看該作者
33#
發(fā)表于 2025-3-27 08:49:20 | 只看該作者
34#
發(fā)表于 2025-3-27 09:48:18 | 只看該作者
35#
發(fā)表于 2025-3-27 16:59:45 | 只看該作者
Research on Urban Street Order Based on Data Mining Technologyamount of problems in urban management are increasing. Under the background of new era, the direction and requirement of the city governance has promoted influenced by the “Internet Plus” strategy and big data strategy. In the construction of information and intelligent construction of urban managem
36#
發(fā)表于 2025-3-27 21:17:50 | 只看該作者
A Vertex-Centric Graph Simulation Algorithm for Large Graphscations such as mining potential associations between users in online social networks. In recent years, graph processing frameworks such as Pregel bring in a vertex-centric, Bulk Synchronous Parallel (BSP) programming model for processing massive data graphs and achieve encouraging results. However,
37#
發(fā)表于 2025-3-28 01:04:26 | 只看該作者
38#
發(fā)表于 2025-3-28 03:41:32 | 只看該作者
Conference proceedings 2018processing and text mining; big data analytics and smart computing; big data applications; the application of big data in machine learning; social networks and recommendation systems; parallel computing and storage of big data; data quality control and data governance; big data system and management..
39#
發(fā)表于 2025-3-28 06:41:13 | 只看該作者
40#
發(fā)表于 2025-3-28 12:25:41 | 只看該作者
Intimate Investments in Drag King Culturesone saliency map. Our network design enables end-to-end training. We validate our algorithm on a vehicle image dataset. Experimental results show that our approach is accurate, fast and robust, and it achieves better performance than other methods.
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