Clustering-Based Algorithms to Semantic Summarizing Graph with Multi-attributes’ Hierarchical Structures

Abstract

K-SGS is a novel graph summarization method which solves the scale limits. By using the concept hierarchy of the nodes' attributes, K-SGS can group the nodes in a flexible way. It groups the nodes not only with same values but also with similar values. Besides the edges' information loss, it also considers the nodes' information loss during the summarization and model the summarization as multi-objective planning. We proposal two hierarchical agglomerative algorithms, one is based on forbearing stratified sequencing method and the other is based on unified objective function method. The experiment on real life dataset shows that our methods can solve the problem and get the graph summaries with good quality.