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2025-08
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Computer and Information Science
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https://doi.org/10.34944/81sm-d094
Abstract
Graph-based methodologies provide powerful tools for uncovering intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in healthcare and power systems.
In healthcare, we examine how social networks influence the attitudes of hemodialysis patients toward kidney transplantation. Using a network-based approach, we investigate how social networks within hemodialysis clinics affect patients' attitudes, contributing to a growing understanding of this dynamic. Our findings emphasize that social networks improve the performance of machine learning models, highlighting the importance of social interactions in clinical settings (Aljurbua et al., 2022). We further introduce Node2VecFuseClassifier, a graph-based model that combines patient interactions with patient characteristics. By comparing problem representations that focus on sociodemographics versus social interactions, we demonstrate that incorporating patient-to-patient and patient-to-staff interactions results in more accurate predictions. This multi-modal analysis, which merges patient experiences with staff expertise, underscores the role of social networks in influencing attitudes toward transplantation (Aljurbua et al., 2024b).
In power systems, we explore the impact of severe weather events that lead to power outages, specifically focusing on predicting weather-induced outages three hours in advance at the county level in the Pacific Northwest of the United States. By utilizing a multi-model multiplex network that integrates data from multiple sources including weather, transmission lines, lightning, vegetation, and social media posts from two leading platforms (Twitter and Reddit), we show how multiplex networks offer valuable insights for predicting power outages. This integration of diverse data sources and network-based modeling emphasizes the importance of leveraging multiple perspectives to enhance the understanding and prediction of power disruptions (Aljurbua et al., 2023). We further present HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. The multiplex network layers of this framework gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. By incorporating multiplex network layers consisting of data collected over time and across regions, we demonstrate that HMN-RTS significantly improves the accuracy of predicting the duration of weather-related outages. This framework enables grid operators to make more reliable predictions up to 6 hours in advance, supporting early risk assessment and proactive mitigation (Aljurbua et al., 2024a, 2025a). Additionally, we introduce SMN-WVF, a spatiotemporal multiplex network designed to predict the duration of power outages in distribution grids. By integrating network-based approach and multi-modal data across space and time, SMN-WVF offers a novel method for predicting disruption durations in distribution grids, enhancing decision-making and mitigation efforts while highlighting the critical role of network-based approaches in forecasting (Aljurbua et al., 2025b).
Overall, our research showcases the potential of graph-based models in tackling complex challenges in both power systems and healthcare. By combining the network-based approach with multi-modal data, we present innovative solutions for predicting power outages and understanding patient attitudes.
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