ChronoConnect: Tracking Pathways Along Highly Dynamic Vertices in Temporal Graphs

With the proliferation of temporal graph data, there is a growing demand for analyzing information propagation patterns during graph evolution. Existing graph analysis systems, mostly based on static snapshots, struggle to effectively capture information flows along the temporal dimension. To address this challenge, we introduce ChronoConnect, a novel system that enables tracking temporal pathways in temporal graph, especially beneficial to downstream mining tasks, e.g., understanding what are the critical pathways in propagating information towards a specific group of vertices. Built on ChronoConnect, users can conveniently configure and execute a variety of temporal traversal algorithms to efficiently analyze information diffusion processes under time constraints. Moreover, ChronoConnect utilizes parallel processing to tackle the explosive size-growth of evolving graphs. We showcase the effectiveness and enhanced performance of ChronoConnect through the implementation of algorithms that track pathways along highly dynamic vertices in temporal graphs. Furthermore, we offer an interactive user interface for graph visualization and query result exploration. We envision ChronoConnect to become a powerful tool for users to examine how information spreads over a temporal graph.