Using and Visualizing Graphs and Graph Algorithms

Representing HEP and astrophysics data as graphs (i.e., networks of related entities) is becoming increasingly popular. These graphs are not only useful for structuring data storage but are also widely used within various machine learning frameworks. Despite their rising popularity, many opportunities remain underutilized, particularly regarding the application of graph algorithms and intuitive visualization techniques. This presentation introduces a comprehensive graph framework designed for handling HEP and astronomical data. The framework supports the storage, manipulation, and analysis of graph data, facilitating the application of fundamental graph algorithms. Additionally, it enables the export of graph data to specialized external toolkits for advanced processing and analysis. A key feature of this framework is its highly interactive, web-based graphical front-end. This interface provides users with deep insights into the graph structures of their data, enabling interactive analysis and multi-faceted visualization of graph properties. It also offers integration capabilities with other related frameworks. The framework’s practical application is demonstrated through its use in analyzing relationships between astronomical alerts, specifically from the Zwicky Transient Facility (ZTF) and the Rubin Observatory. By leveraging the collective properties and relationships within these data, the framework facilitates comprehensive analyses and provides recommendations based on object similarities and neighborhood characteristics. This approach paves the way for novel insights and methodologies in data-driven research.