Over the last decade, there has been considerable interest in designing algorithms for
processing massive graphs in the data stream model. The original motivation was two-fold …

When processing massive data sets, a core task is to construct synopses of the data. To be
useful, a synopsis data structure should be easy to construct while also yielding good …

We initiate the study of graph sketching, ie, algorithms that use a limited number of linear
measurements of a graph to determine the properties of the graph. While a graph on n …

There is a rapidly growing need for scalable algorithms that solve classical graph problems,
such as maximum matching and minimum vertex cover, on massive graphs. For massive …

We present the first single pass algorithm for computing spectral sparsifiers for graphs in the
dynamic semi-streaming model. Given a single pass over a stream containing insertions and …

We present the first data structures that maintain near optimal maximum cardinality and
maximum weighted matchings on sparse graphs in sub linear time per update. Our main …

In this paper we present a simple but powerful subgraph sampling primitive that is
applicable in a variety of computational models including dynamic graph streams (where the …

We present a deterministic (1+ ε)-approximate maximum matching algorithm in poly (1/ε)
passes in the semi-streaming model, solving the long-standing open problem of breaking …

We study the problem of finding a maximum matching in a graph given by an input stream
listing its edges in some arbitrary order, where the quantity to be maximized is given by a …

We study the problem of finding an approximate maximum matching in two closely related
computational models, namely, the dynamic graph streaming model and the simultaneous …