Graph Summarization Methods and Applications: A Survey

TL;DR

This survey reviews current methods for graph data summarization, highlighting their challenges, categories, applications, and open problems, to facilitate better understanding and processing of complex interconnected data.

Contribution

It provides a comprehensive, structured overview of state-of-the-art graph summarization techniques, categorizing approaches and discussing real-world applications and open issues.

Findings

Categorizes graph summarization methods by input graph type and methodology

Highlights applications in real-world data analysis and visualization

Identifies open problems and future research directions in the field

Abstract

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.