"基于聚类和K2-tree的大规模图数据压缩表示技术研究"

The research paper titled "Computing Research - Large-Scale Graph Data Compression Representation Technology Based on Clustering and K2-Tree" explores the challenges and opportunities presented by the massive amount of data generated and accumulated in various emerging applications due to the rapid development of Internet technology and increase in users. Graph data, as a significant type of big data, is crucial in the analysis of the Internet, social networks, and other fields. However, the sheer volume of graph data poses challenges in terms of storage, processing, and analysis. The paper proposes a novel approach to compressing large-scale graph data using clustering and K2-Tree. Clustering is used to group similar nodes together, reducing redundancy and improving compression efficiency. K2-Tree, a data structure optimized for storing graphs, is then employed to represent the compressed graph data in a space-efficient manner. This combination of clustering and K2-Tree allows for efficient storage and retrieval of large-scale graph data while preserving the essential structural information. The research conducted in this paper contributes to the development of techniques for handling and analyzing massive amounts of graph data. By effectively compressing graph data, it enables faster processing and analysis, leading to valuable insights and discoveries in various fields relying on graph data. The proposed approach has the potential to address the challenges posed by the exponential growth of data in the digital age, opening up new possibilities for data-driven research and innovation.