https://scholars.lib.ntu.edu.tw/handle/123456789/581098
Title: | Quality-Aware Streaming Network Embedding with Memory Refreshing | Authors: | Chen H.-W Shuai H.-H Wang S.-D Yang D.-N. SHENG-DE WANG |
Keywords: | Data mining; Learning frameworks; Named graphs; Re-computing; Real networks; Static networks; Streaming networks; Structural information; Structure information; Embeddings | Issue Date: | 2020 | Journal Volume: | 12084 LNAI | Start page/Pages: | 448-461 | Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Abstract: | 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 |
Appears in Collections: | 電機工程學系 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.