Streaming Network Embedding through Local Actions
Xi Liu, Ping-Chun Hsieh, Nick Duffield, Rui Chen, Muhe Xie, Xidao Wen

TL;DR
This paper introduces a novel streaming network embedding framework that efficiently updates vertex features in dynamic networks using an online approximation method, addressing the challenges of high complexity and lack of closed-form solutions.
Contribution
The paper develops a new online approximation approach for streaming network embedding that efficiently updates features without retraining or relying on vertex attributes.
Findings
Efficiently updates vertex features in streaming networks.
Achieves comparable or better classification and clustering performance.
Operates with low complexity and high efficiency.
Abstract
Recently, considerable research attention has been paid to network embedding, a popular approach to construct feature vectors of vertices. Due to the curse of dimensionality and sparsity in graphical datasets, this approach has become indispensable for machine learning tasks over large networks. The majority of existing literature has considered this technique under the assumption that the network is static. However, networks in many applications, nodes and edges accrue to a growing network as a streaming. A small number of very recent results have addressed the problem of embedding for dynamic networks. However, they either rely on knowledge of vertex attributes, suffer high-time complexity or need to be re-trained without closed-form expression. Thus the approach of adapting the existing methods to the streaming environment faces non-trivial technical challenges. These challenges…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Domain Adaptation and Few-Shot Learning
