Utility-Based Graph Summarization: New and Improved

TL;DR

This paper introduces scalable, utility-preserving graph summarization techniques that enable lossless and lossy compression, supporting efficient querying on web-scale graphs with minimal utility loss and no need for graph reconstruction.

Contribution

It proposes a novel utility-driven graph summarization method with superior compression and speed, and scalable algorithms for lossy summarization that handle web-scale graphs on a single machine.

Findings

Lossless summarization achieves better compression than state-of-the-art.

Lossy summarization is scalable and handles web-scale graphs efficiently.

Summaries support direct query answering without graph reconstruction.

Abstract

A fundamental challenge in graph mining is the ever-increasing size of datasets. Graph summarization aims to find a compact representation resulting in faster algorithms and reduced storage needs. The flip side of graph summarization is the loss of utility which diminishes its usability. The key questions we address in this paper are: (1)How to summarize a graph without any loss of utility? (2)How to summarize a graph with some loss of utility but above a user-specified threshold? (3)How to query graph summaries without graph reconstruction?} We also aim at making graph summarization available for the masses by efficiently handling web-scale graphs using only a consumer-grade machine. Previous works suffer from conceptual limitations and lack of scalability. In this work, we make three key contributions. First, we present a utility-driven graph summarization method, based on a clique and independent set decomposition, that produces significant compression with zero loss of utility. The compression provided is significantly better than state-of-the-art in lossless graph summarization, while the runtime is two orders of magnitude lower. Second, we present a highly scalable algorithm for the lossy case, which foregoes the expensive iterative process that hampers previous work. Our algorithm achieves this by combining a memory reduction technique and a novel binary-search approach. In contrast to the competition, we are able to handle web-scale graphs in a single machine without a performance impediment as the utility threshold (and size of summary) decreases. Third, we show that our graph summaries can be used as-is to answer several important classes of queries, such as triangle enumeration, Pagerank, and shortest paths. This is in contrast to other works that incrementally reconstruct the original graph for answering queries, thus incurring additional time costs.