Constrained Utility Driven Graph Summarization

The substantial volume of data impedes rapid processing. So graph summarization is employed to reduce graph size while preserving key features. This paper aims to achieve graph summarization with a high compression rate, retaining essential information. Previous works focus on either maintaining neighborhood or safeguarding important data. An aggregated objective function is introduced to create a summarized graph that maintains useful information and node neighborhoods. A tree based algorithm is proposed to align with this objective function. The experimental findings indicate that the proposed algorithm outperforms its competitors in terms of standard evaluation measures by achieving a higher summarization rate and maintaining utility.