The clustering method based on graph models has garnered increased attention
for its widespread applicability across various knowledge domains. Its
adaptability to integrate seamlessly with other relevant applications endows
the graph model-based clustering analysis with the ability to robustly extract
"natural associations" or "graph structures" within datasets, facilitating the
modelling of relationships between data points. Despite its efficacy, the
current clustering method utilizing the graph-based model overlooks the
uncertainty associated with random walk access between nodes and the embedded
structural information in the data. To address this gap, we present a novel
Clustering method for Maximizing Decoding Information within graph-based
models, named CMDI. CMDI innovatively incorporates two-dimensional structural
information theory into the clustering process, consisting of two phases: graph
structure extraction and graph vertex partitioning. Within CMDI, graph
partitioning is reformulated as an abstract clustering problem, leveraging
maximum decoding information to minimize uncertainty associated with random
visits to vertices. Empirical evaluations on three real-world datasets
demonstrate that CMDI outperforms classical baseline methods, exhibiting a
superior decoding information ratio (DI-R). Furthermore, CMDI showcases
heightened efficiency, particularly when considering prior knowledge (PK).
These findings underscore the effectiveness of CMDI in enhancing decoding
information quality and computational efficiency, positioning it as a valuable
tool in graph-based clustering analyses.

CMDI 是一种基于图模型的聚类方法，通过将二维结构信息理论纳入聚类过程，提高解码信息质量和计算效率，从而成为图形聚类分析中的有价值工具。