EvoNRL: Evolving Network Representation Learning Based on Random Walks

  • Farzaneh HeidariEmail author
  • Manos Papagelis
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)


Large-scale network mining and analysis is key to revealing the underlying dynamics of networks. Lately, there has been a fast-growing interest in learning random walk-based low-dimensional continuous representations of networks. While these methods perform well, they can only operate on static networks. In this paper, we propose a random-walk based method for learning representations of evolving networks. The key idea of our approach is to maintain a set of random walks that are consistently valid with respect to the updated network topology. This way we are able to continuously learn a new mapping function from the new network to the existing low-dimension network representation. A thorough experimental evaluation is performed that demonstrates that our method is both accurate and fast, for a varying range of conditions.


Network representation Evolving networks Random walks 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.York UniversityTorontoCanada

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