https://scholars.lib.ntu.edu.tw/handle/123456789/581098
標題: | Quality-Aware Streaming Network Embedding with Memory Refreshing | 作者: | Chen H.-W Shuai H.-H Wang S.-D Yang D.-N. SHENG-DE WANG |
關鍵字: | Data mining; Learning frameworks; Named graphs; Re-computing; Real networks; Static networks; Streaming networks; Structural information; Structure information; Embeddings | 公開日期: | 2020 | 卷: | 12084 LNAI | 起(迄)頁: | 448-461 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Static network embedding has been widely studied to convert sparse structure information into a dense latent space. However, the majority of real networks are continuously evolving, and deriving the whole embedding for every snapshot is computationally intensive. To avoid recomputing the embedding over time, we explore streaming network embedding for two reasons: 1) to efficiently identify the nodes required to update the embeddings under multi-type network changes, and 2) to carefully revise the embeddings to maintain transduction over different parts of the network. Specifically, we propose a new representation learning framework, named Graph Memory Refreshing (GMR), to preserve both global types of structural information efficiently. We prove that GMR maintains the consistency of embeddings (crucial for network analysis) for isomorphic structures better than existing approaches. Experimental results demonstrate that GMR outperforms the baselines with much smaller time. ? Springer Nature Switzerland AG 2020. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085733996&doi=10.1007%2f978-3-030-47426-3_35&partnerID=40&md5=bd9002253da80ac97aacb0552af1fe70 https://scholars.lib.ntu.edu.tw/handle/123456789/581098 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-47426-3_35 |
顯示於: | 電機工程學系 |
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