  The network alignment (or graph matching) problem refers to recovering the
node-to-node correspondence between two correlated networks. In this paper, we
propose a network alignment algorithm which works without using a seed set of
pre-matched node pairs or any other auxiliary information (e.g., node or edge
labels) as an input. The algorithm assigns structurally innovative features to
nodes based on the tail of empirical degree distribution of their neighbor
nodes. Then, it matches the nodes according to these features. We evaluate the
performance of proposed algorithm on both synthetic and real networks. For
synthetic networks, we generate Erdos-Renyi graphs in the regions of
$\Theta(\log(n)/n)$ and $\Theta(\log^{2}(n)/n)$, where a previous work
theoretically showed that recovering is feasible in sparse Erdos-Renyi graphs
if and only if the probability of having an edge between a pair of nodes in one
of the graphs and also between the corresponding nodes in the other graph is in
the order of $\Omega(\log(n)/n)$, where $n$ is the number of nodes. Experiments
on both real and synthetic networks show that it outperforms previous works in
terms of probability of correct matching.
