Identifying conflict hotspots in Papua through integrated graph theory and clustering analysis

Abstract Conflict in Papua has long attracted academic and policy attention, yet most studies remain descriptive and qualitative, leaving gaps in predictive capacity and quantitative validation. This study addresses these limitations by applying an integrated computational framework that combines graph-based modeling and clustering algorithms to conflict data recorded between 2018 and 2024. The aim is to identify central nodes in the conflict network, statistically validate spatial hotspots, and bridge social sciences with computational analysis. The research design is exploratory and quantitative. Conflict incidents were compiled from Human Rights Monitor (HRM) and Komnas HAM RI reports, then standardized into adjacency matrices and spatial coordinates. Using graph theory, centrality measures (degree and betweenness) and modularity were computed to determine structural hubs and community partitions within the conflict network. In parallel, clustering methods K-Means and DBSCAN were applied to classify districts into high, medium, and low conflict clusters. Robustness was tested through Silhouette Scores, ensuring statistical validation of hotspot identification. Results indicate a clear escalation of conflict from 2018 to 2024, with violent clashes rising from 5 to 20 incidents and displacement cases increasing twentyfold. Yahukimo, Intan Jaya, and Nduga consistently emerged as central hubs. Graph analysis confirmed Yahukimo’s dual role as hub and broker (degree centrality = 0.29, betweenness = 0.38), while modularity (Q ≈ 0.42) revealed distinct community partitions, especially between highland and western districts. Clustering validation yielded Silhouette Scores above 0.6 for high-intensity clusters, demonstrating strong cohesion and separation. The novelty of this study lies in its integration of graph-theoretic metrics and unsupervised clustering algorithms to build a hybrid quantitative model for conflict hotspot prediction in Papua. Previous works have typically relied on narrative or GIS-based spatial mapping without structural validation; this research introduces a dual-layer analytical approach that not only detects but also explains inter-district linkages and community modularity within the conflict network. The framework thus transforms descriptive conflict data into an interpretable mathematical topology, capable of generating early-warning indicators and policy-relevant insights.This research contributes in three ways. First, it provides quantitative indicators of conflict centrality, advancing beyond descriptive accounts. Second, it statistically validates conflict hotspots, distinguishing sustained clusters from sporadic outliers such as Fakfak and Tambrauw. Third, it demonstrates the value of integrating graph theory with clustering analysis, offering a predictive and holistic framework for conflict studies. The methodological novelty reinforces the potential of computational social science in Indonesia’s conflict research landscape, marking a shift from post-factum documentation toward proactive, data-driven conflict modeling.