  Graph sampling plays an important role in data mining for large networks.
Specifically, larger networks often correspond to lower sampling rates. Under
the situation, traditional traversal-based samplings for large networks usually
have an excessive preference for densely-connected network core nodes. Aim at
this issue, this paper proposes a sampling method for unknown networks at low
sampling rates, called SLSR, which first adopts a random node sampling to
evaluate a degree threshold, utilized to distinguish the core from periphery,
and the average degree in unknown networks, and then runs a double-layer
sampling strategy on the core and periphery. SLSR is simple that results in a
high time efficiency, but experimental evaluation confirms that the proposed
method can accurately preserve many critical structures of unknown large
networks with low sampling rates and low variances.
