Graph Summarization: Algorithms, Trained Heuristics, and Practical Storage Application
George M Hodulik · OhioLink ETD Center (Ohio Library and Information Network) · 2017
The problem of graph summarization has practical applications involving visualization and graph compression.As graph-structured databases become popular and large, summarizing and compressing graph-structured databases can become more and more useful.We explore the use of a particular family of graph summarization algorithms we call Summaries with Supernodes, Superedges, and Corrections (SSSC) and the feasibility of using SSSC algorithms when summarizing large Resource Description Framework (RDF) graph datasets.We also propose optimizations to the Uniform Randomized SSSC algorithm by using trained heuristics to pick seed nodes.We also show how SSSC summaries may be stored in a similar manner as RDF triple stores, and we discuss possibilities for future work involving localized SSSC algorithms.