Quality-Aware Streaming Network Embedding with Memory Refreshing

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
EditorsHady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
Number of pages14
ISBN (Print)9783030474256
StatePublished - 1 Jan 2020
Event24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12084 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020


  • Network embedding
  • Streaming data mining

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