Mapping Collaborative Governance in Poverty Data Management Through a Social Network Analysis Model in Bogor City, Indonesia

Poverty is a complex issue that remains a primary challenge for the Bogor City Government. Low validity and accuracy of poverty data, overlapping data sources, and weak inter-agency coordination hinder the formulation of effective poverty alleviation policies. This study aims to develop a collaborative governance model for poverty data management, utilizing a Social Network Analysis (SNA) approach. The research employs a mixed-methods design—incorporating both qualitative and quantitative approaches—alongside an analysis of secondary data from relevant policy documents and regulations. SNA was conducted using the Gephi software to map network structures, identify key actors, and determine centrality levels within the collaborative framework. The findings indicate that the network requires key actors—those with the most extensive, strongest, and most effective connections—to serve as information disseminators and connectors among all stakeholders. Collaborative governance is shown to enhance trust, transparency, and inter-agency synergy, thereby improving the quality of poverty data and supporting policies based on a "single data" (one-data) principle. This research underscores the critical importance of data integration and cross-actor collaboration in achieving accountable, transparent, and effective social governance for poverty alleviation at the local level.