Streaming Graph Computations with a Helpful Advisor

G. Cormode, M. Mitzenmacher, and J. Thaler


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 space (measured in words) is needed for single-pass algorithms given linear-sized annotations. We also obtain protocols achieving essentially optimal tradeoffs between annotation length and memory usage for several important problems, including integer matrix-vector multiplication, as well as shortest s-t path in small-diameter graphs. We also obtain non-trivial tradeoffs for minimum weight bipartite perfect matching and shortest s-t path in general graphs.