Beyond Missing Data: A Multi-Scale Graph Fusion Framework for Sustainable Development Insights

In the context of sustainable development, particularly in environmental monitoring and resource management, data from multiple heterogeneous sources are often incomplete or inconsistent. This presents a significant challenge for data-driven analysis, especially in tasks like clustering, where the goal is to extract meaningful patterns from multi-view data. Incomplete multi-view clustering (IMVC) aims to address this challenge by effectively leveraging complementary and consistent information despite the missing data. However, traditional graph-based clustering methods that rely on Euclidean distance often fail to capture the complex structures in high-dimensional incomplete data. To overcome this limitation, we propose Motif-Based Multi-Scale Bipartite Graph Fusion (MMBGF_IMC), a novel framework that combines multi-scale measurements with ensemble clustering. By integrating higher-order graph motifs, MMBGF_IMC significantly enhances the representation of inter-instance correlations. Empirical results on seven real-world datasets demonstrate that MMBGF_IMC outperforms existing methods by an average of 5–15% in clustering accuracy (ACC) and normalized mutual information (NMI), offering an effective solution for data fusion, modeling, and mining in sustainable development applications such as ecological monitoring, urban planning, and resource management.