Densest Subgraph in Dynamic Graph Streams

Abstract

In this paper, we consider the problem of approximating the densest subgraph in the dynamic graph stream model. In this model of computation, the input graph is defined by an arbitrary sequence of edge insertions and deletions and the goal is to analyze properties of the resulting graph given memory that is sub-linear in the size of the stream. We present a single-pass algorithm that returns a $(1+\epsilon )$ approximation of the maximum density with high probability; the algorithm uses $O(\epsilon ^{-2} n {{\mathrm{polylog}}}~n)$ space, processes each stream update in ${{\mathrm{polylog}}}(n)$ time, and uses ${{\mathrm{poly}}}(n)$ post-processing time where n is the number of nodes. The space used by our algorithm matches the lower bound of Bahmani et al. (PVLDB 2012) up to a poly-logarithmic factor for constant $\epsilon$. The best existing results for this problem were established recently by Bhattacharya et al. (STOC 2015). They presented a $(2+\epsilon )$ approximation algorithm using similar space and another algorithm that both processed each update and maintained a $(4+\epsilon )$ approximation of the current maximum density in ${{\mathrm{polylog}}}(n)$ time per-update.

This work was supported by NSF Awards CCF-0953754, IIS-1251110, CCF-1320719, and a Google Research Award.