Compressing data on graphs with clusters.

Amir R. Asadi, Emmanuel Abbe, Sergio Verdú

Abstract: This paper investigates the fundamental limits for compressing data on graphs, exploiting dependencies due to community structures in the graph. The source model, referred to as the data block model (DBM), is a mixture of discrete memoryless sources determined by the community structure of a stochastic block model (SBM). The main result gives the optimal expected length of a lossless compressor when the community signal is strong enough, a condition on the edge probabilities and the data distributions, which can take place below the exact recovery threshold of the SBM. This is derived in part by obtaining the threshold for exact recovery in SBMs with strong side information, a result of independent interest, which extends the CH-divergence threshold. Finally we discuss compressing data with almost exact recovery algorithms.