Titlebook: Computer Supported Cooperative Work and Social Computing; 18th CCF Conference, Yuqing Sun,Tun Lu,Bowen Du Conference proceedings 2024 The E

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書(shū)目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)




書(shū)目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computer Supported Cooperative Work and Social Computing被引頻次




書(shū)目名稱Computer Supported Cooperative Work and Social Computing被引頻次學(xué)科排名




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書(shū)目名稱Computer Supported Cooperative Work and Social Computing年度引用學(xué)科排名




書(shū)目名稱Computer Supported Cooperative Work and Social Computing讀者反饋




書(shū)目名稱Computer Supported Cooperative Work and Social Computing讀者反饋學(xué)科排名

鎮(zhèn)痛劑

HGNN-T5 PEGASUS: A Hybrid Approach for?Chinese Long Text Summarizations introduced to T5 PEGASUS to avoid overfitting. To demonstrate the effectiveness of our model, we constructed the SCHOLAT text summarization dataset. The results of our experiments show that the proposed model outperforms other baseline models on both the NLPCC 2018 and SCHOLAT datasets.

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禍害隱伏

Similarity Metrics and Visualization of Scholars Based on Variational Graph Normalized Auto-Encodersted for experiments in this paper. The experimental results show that the model achieves the best performance on both the AUC and AP metrics on the task of measuring scholar similarity, with 98.7% and 98.8%, respectively, relative to other traditional widely used algorithms. In addition, by fusing t

Mets552

Scholar Influence Maximization via?Opinion Leader and?Graph Embedding Regression in?Social Networksy overlapping influence ranges between the highly influential nodes in the seed set. Specifically, our framework first adopts an information diffusion model-based approach to obtain opinion leaders’ tendency evaluation and meanwhile uses variational graph auto-encoders (VGAE) to encode it into low-d

Mets552

https://doi.org/10.1007/978-94-6300-010-9s introduced to T5 PEGASUS to avoid overfitting. To demonstrate the effectiveness of our model, we constructed the SCHOLAT text summarization dataset. The results of our experiments show that the proposed model outperforms other baseline models on both the NLPCC 2018 and SCHOLAT datasets.

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