  The existing methods to calculate the Eigenvector Centrality(EC) tend to not
be robust enough for determination of EC in low time complexity or not
well-scalable for large networks, hence rendering them practically unreliable/
computationally expensive. So, it is of the essence to develop a method that is
scalable in low computational time. Hence, we propose a deep learning model for
the identification of nodes with high Eigenvector Centrality. There have been a
few previous works in identifying the high ranked nodes with supervised
learning methods, but in real-world cases, the graphs are not labelled and
hence deployment of supervised learning methods becomes a hazard and its usage
becomes impractical. So, we devise CUL(Centrality with Unsupervised Learning)
method to learn the relative EC scores in a network in an unsupervised manner.
To achieve this, we develop an Encoder-Decoder based framework that maps the
nodes to their respective estimated EC scores. Extensive experiments were
conducted on different synthetic and real-world networks. We compared CUL
against a baseline supervised method for EC estimation similar to some of the
past works. It was observed that even with training on a minuscule number of
training datasets, CUL delivers a relatively better accuracy score when
identifying the higher ranked nodes than its supervised counterpart. We also
show that CUL is much faster and has a smaller runtime than the conventional
baseline method for EC computation. The code is available at
https://github.com/codexhammer/CUL.
