FLUID: A common model for semantic structural graph summaries based on equivalence relations

Summarization is a widespread method for handling very large graphs. The task of structural graph summarization to compute concise but meaningful synopsis the key information graph. As summaries may be used many different purposes, there no single concept or model summaries. We have studied existing large-scale (semantic) Despite their concepts and we found commonalities in structures they capture. use these provide first time formally defined common model, FLUID (FLexible sUmmarIes Data graphs), that allows us flexibly define quickly defined, adapted, compared purposes datasets. To this end, provides features based on equivalence relations such as distinction types properties, direction edges, bisimulation, inference. conduct detailed complexity analysis provided by FLUID. show with can computed worst case O(n2) w.r.t. n, number edges data An empirical web graphs billions indicates typical running ?(n). Based formal one modify various from literature beyond.