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Co-Hub Node Based Multiview Graph Learning With Theoretical Guarantees
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DOI：10.1109/tsp.2026.3729139.png)
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Abstract
En 中文
Identifying the graphical structure underlying observed multivariate data is essential in numerous applications. Current methodologies are predominantly confined to deducing a single graph under the presumption that the observed data has uniformly sparse connections. However, many applications involve heterogeneous datasets that feature multiple closely related graphs, typically referred to as multiview graphs. Previous research on multiview graph learning promotes edge-based similarity across layers using pairwise or consensus-based regularizers. However, multiview graphs frequently exhibit a shared node-based architecture across different views, such as common hub nodes. Such commonalities can enhance the precision of learning and provide interpretive insight. In this paper, we propose a co-hub node model, positing that different views share a common group of hub nodes. The associated optimization framework is developed by enforcing structured sparsity on the connections of these co-hub nodes. Moreover, we present a theoretical examination of layer identifiability and determine bounds on estimation error. The proposed methodology is validated using both synthetic graph data and fMRI time series data from multiple subjects to discern several closely related graphs.
Keywords:
Multiview graphs
graph learning
co-hub nodes
brain network
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