| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 107 === Static network embedding has been widely studied to convert the sparse structure information to a dense latent space for various applications. However, real networks are continuously evolving, and deriving the whole embedding for every snapshot is computationally intensive. In this paper, therefore, we explore streaming network embedding to 1) efficiently identify the nodes required to update the embeddings under multi-type network changes and 2) 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 structural information and embedding consistency for streaming network embedding. We prove that GMR is more consistent than other state-of-the-art methods. Experimental results manifest that GMR outperforms the baselines in both the accuracy and the running time.
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