標題: | On Accelerating Multi-Layered Heterogeneous Network Embedding Learning |
作者: | Shuai, Hong-Han Tsai, Cheng-Ming Hsu, Yun-Jui Hsiao, Ta-Che 電機工程學系 Department of Electrical and Computer Engineering |
關鍵字: | Heterogeneous information network;graph embedding;acceleration |
公開日期: | 1-一月-2018 |
摘要: | With the rapid growth of online social networks and IoT networks, mining valuable knowledge from the graph data become important. Meanwhile, as machine learning algorithms show their powers in prediction, different machine learning algorithms are proposed for different applications, e.g., personal recommendation, price prediction, communication anomaly detection. However, it is challenging to extract network features from graph data as the inputs for machine learning algorithms. One of the promising approaches is to use graph embedding approach, which extracts the valuable information of networks from each node into low dimensional vectors. However, the graph embedding approaches on a large-scale network require tremendous training time. Therefore, in this paper, we propose NOde Differentiation for Graph Embedding (NODGE) to prioritize the nodes, while high priority nodes are allocated with more resources to train their representations. We also theoretically analyze the proposed NODGE. Experimental results show that the proposed method reduces the training time of state-of-the-art method by at least 30.7%. |
URI: | http://hdl.handle.net/11536/152457 |
ISBN: | 978-1-5386-4727-1 |
ISSN: | 2334-0983 |
期刊: | 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
起始頁: | 0 |
結束頁: | 0 |
顯示於類別: | 會議論文 |