ChronoConnect: Tracking Pathways Along Highly Dynamic Vertices in Temporal Graphs

2025-12-29

AI Summary

Static snapshot-based approaches fail to capture temporal information flow and the evolution of propagation paths in time-evolving graphs. To address this, we propose the first temporal path tracing system designed for highly dynamic vertices. Our method abandons the conventional static graph snapshot paradigm and instead supports configurable, time-constrained path traversal algorithms, integrated with parallel graph processing and web-based interactive visualization. At the modeling level, we adopt an exact temporal graph representation that preserves fine-grained temporal semantics. Computationally, we enable efficient, low-latency path queries over large-scale evolving graphs. Analytically, our system significantly improves both the accuracy and real-time responsiveness of critical propagation path identification. Empirical evaluation demonstrates that our system outperforms state-of-the-art methods in both query efficiency and interpretability, establishing a new benchmark for temporal path analysis in dynamic networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

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.

Problem

• Tracking temporal pathways in dynamic graphs for information flow analysis.
• Analyzing information diffusion processes under time constraints efficiently.
• Handling explosive size-growth of evolving graphs with parallel processing.

Innovation

• Tracks temporal pathways in dynamic graph evolution
• Utilizes parallel processing for large-scale graph growth
• Provides interactive visualization for query exploration