In this paper, we design sub-linear space streaming algorithms for estimating three fundamental parameters -- maximum independent set, minimum dominating set and maximum matching -- on sparse graph classes, i.e., graphs which satisfy $m=O(n)$ where $m,n$ is the number of edges, vertices respectively. Each of the three graph parameters we consider can have size $\Omega(n)$ even on sparse graph classes, and hence for sublinear-space algorithms we are restricted to parameter estimation instead of attempting to find a solution.

翻译：本文针对稀疏图类（即满足$m=O(n)$的图，其中$m$和$n$分别为边数和顶点数）设计了用于估计三个基本参数——最大独立集、最小支配集和最大匹配——的次线性空间流式算法。我们考虑的每个图参数即使在稀疏图类上也可能达到$\Omega(n)$的量级，因此对于次线性空间算法，我们局限于参数估计而非尝试求解。