Aggregation-Based Attributed Graph Summarization
Bochuan Song, Bo Chai, Yuhuai Wang, Zhipeng Yang, Shang Yang · 2020
Graph summarization has a wide range of applications that involve many large-scale graph data processing problems. However, most of existing approaches for graph summarization neglect attributes and relationships residing in nodes and edges in graphs. In this paper, an aggregation-based attributed graph summarization approach is proposed by fully taking attributes and relationships into account during graph summarization. We present a new approach to quantitatively calculate node merge error, and a heuristic measure to dynamically determine the threshold of node merge error per iteration. We implemented our aggregation-based attributed graph summarization approach. Extensive experiments were made for illustrating effectiveness and efficiency of our approach in comparison with the state-of-the-art approach based on graph topology.