Applications of Uniform Sampling: Densest Subgraph and Beyond

Recently [Bhattacharya et al., STOC 2015] provide the first non-trivial algorithm for the densest subgraph problem in the streaming model with additions and deletions to its edges, i.e., for dynamic graph streams. They present a (0.5-ε)-approximation algorithm using tildeO(n) space, where factors of ε and log(n) are suppressed in the tildeO notation. However, the update time of this algorithm is large. To remedy this, they also provide a (0.25-ε)-approximation algorithm using tildeO(n) space with update time tildeO(1). In this paper we improve the algorithms by Bhattacharya et al. by providing a (1-ε)-approximation algorithm using tildeO(n) space. Our algorithm is conceptually simple - it samples tildeO(n) edges uniformly at random, and finds the densest subgraph on the sampled graph. We also show how to perform this sampling with update time tildeO(1). In addition to this, we show that given oracle access to the edge set, we can implement our algorithm in time tildeO(n) on a graph in the standard RAM model. To the best of our knowledge this is the fastest (0.5-ε)-approximation algorithm for the densest subgraph problem in the RAM model given such oracle access. Further, we extend our results to a general class of graph optimization problems that we call heavy subgraph problems. This class contains many interesting problems such as densest subgraph, directed densest subgraph, densest bipartite subgraph, d-cut and d-heavy connected component. Our result, by characterizing heavy subgraph problems, partially addresses open problem 13 at the IITK Workshop on Algorithms for Data Streams in 2006 regarding the effects of subsampling in this context.