Community Detection In Sparse Networks

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Laala Zeyneb, Belguerna Abderrahmane


Spectral methods in which they are based on matrix eigenvectors are widely
used in network data analysis, especially for community detection. These
classical approaches are based on graph associated matrix (adjacency matrix)
and related matrices, nevertheless go wrong with sparse networks, which they
have a lot of interest in practice. The spectrum of the non-backtracking
matrix, an alternate matrix representation of a network that shows a behavior
in the sparse limit, has recently been presented as a solution to this problem.
However, the use of this matrix was limited for a specified number of
communities. We are presenting a matrix for the graph and showing that it
can be using for different number of communities.

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