A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction

The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by combining joint graph learning with network analysis. Motivated by this discovery, we develop a Conjoint Graph Representation Learning (CGRL) framework that: a) constructs two networks based on disease coding, including the patient network and the disease difference network. Three comorbidity network features were generated based on the basic difference network to capture the potential relationship between comorbidities and risk diseases; b) incorporates computational structure intervention and learning feature representation, CGRL was developed to predict the risks of diabetes and coronary heart disease in patients; and c) analysis the comorbidity patterns and exploring the pathways of disease progression, the pathological pathogenesis of diabetes and coronary heart disease may be revealed. The results show that the network features extracted based on the difference network are important, and the framework we proposed provides more accurate predictions than other strong models in terms of accuracy.

Graph Clustering with Cross-View Feature Propagation

Graph clustering is a fundamental and challenging learning task, which is conventionally approached by grouping similar vertices based on edge structure and feature similarity.In contrast to previous methods, in this paper, we investigate how multi-view feature propagation can influence cluster discovery in graph data.To this end, we present Graph Clustering With Cross-View Feature Propagation (GCCFP), a novel method that leverages multi-view feature propagation to enhance cluster identification in graph data.GCCFP employs a unified objective function that utilizes graph topology and multi-view vertex features to determine vertex cluster membership, regularized by a module that supports key latent feature propagation. We derive an iterative algorithm to optimize this function, prove model convergence within a finite number of iterations, and analyze its computational complexity. Our experiments on various real-world graphs demonstrate the superior clustering performance of GCCFP compared to well-established methods, manifesting its effectiveness across different scenarios.