Authors:
Pharsana Parveen M.,Stanis Arul Mary A.,DOI NO:
https://doi.org/10.26782/jmcms.2026.05.00012Keywords:
Graph-Theoretic Modeling,Dynamic Centrality,Symptom Networks,Mental Health,Hysteresis,Abstract
This study addresses a significant gap in mental health research by developing a computational algorithm that goes beyond existing traditional symptom analysis. Instead of treating mental health symptoms as isolated phenomena, we created a methodology that captures their complex interconnected nature. We developed the Dynamic Weighted Centrality Hysteresis Symptoms Network Algorithm (DWCHSN), which applies concepts in network science to mental health symptomology. The DWCHSN algorithm effectively detects and ranks symptoms based on their centrality and influence that collectively capture how symptoms activate, spread, self-reinforce, persist, and respond to intervention within the network. This helps clinicians in setting treatment priorities by identifying the symptoms that are important catalysts. Our algorithm connects theoretical psychopathology models with clinical practice, unlike conventional diagnostic frameworks that list symptoms without considering their relationships.Refference:
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