Graphs” Sampling for Graph Approximation § Sparsification extensively studied in graph theory § Idea: approximate the graph using a sparse, much smaller graph § Many computationally intensive § Not amenable to distributed implementation § Build on Spielman & Teng’s work* § Keep edges with probability cial properties of the input graph. While several proposals on the type of sparsier exists, many o m are either computationally intensive, or are not amenable to stributed implementation (which is the focus of our work)3. A nitial solution, we developed a simple sparsier adapted from work of Spielman and Teng [31] that is based on vertex degree sparsier uses the following probability to decide to keep an e between vertex a and b: dAV G ⇥ s min(d o a,d i b ) (1 o Exploring other sparsification techniques