## G. Cormode, M. Mitzenmacher, and J. Thaler.
Streaming graph computations with a helpful advisor.
In *European Symposium on Algorithms*, 2010.

Motivated by the trend to outsource work to commercial cloud computing
services, we consider a variation of the streaming paradigm where a
streaming algorithm can be assisted by a powerful helper that can
provide annotations to the data stream. We extend previous work on
such *annotation models* by considering a number of graph
streaming problems. Without annotations, streaming algorithms for
graph problems generally require significant memory; we show that
for many standard problems, including all graph problems that can
be expressed with totally unimodular integer programming formulations,
only constant memory is
needed for single-pass algorithms given linear-sized annotations.
We also obtain a protocol achieving *optimal* tradeoffs between
annotation length and memory usage for matrix-vector multiplication;
this result contributes to a trend of recent research on numerical
linear algebra in streaming models.

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