Quality-Aware Streaming Network Embedding with Memory Refreshing

Hsi Wen Chen, Hong Han Shuai, Sheng De Wang, De Nian Yang*

*此作品的通信作者

研究成果: Conference contribution同行評審

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.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
編輯Hady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
發行者Springer
頁面448-461
頁數14
ISBN(列印)9783030474256
DOIs
出版狀態Published - 1 1月 2020
事件24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
持續時間: 11 5月 202014 5月 2020

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12084 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
國家/地區Singapore
城市Singapore
期間11/05/2014/05/20

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