Efficient Temporal Edge-Core Maintenance in Streaming Graphs

Temporal graphs are critical for modeling dynamic systems where interactions evolve over time, with a central challenge being the characterization of structural cohesion. The temporal edge-core , defined under a temporal proximity constraint Δ, quantifies the stability and density of connections within subgraphs and is essential for applications such as anomaly detection and information diffusion. Existing edge-core decomposition methods, however, are designed for static graphs and are computationally prohibitive in streaming environments due to frequent edge arrivals and deletions. We present TECM , an efficient framework for streaming temporal edge-core decomposition that leverages the localized impact of edge updates within Δ-incident neighbors. TECM incrementally updates core values through Δ-aware traversals and localized H-index analysis, and incorporates batch processing to handle high-velocity streams. Extensive experiments on real and synthetic temporal networks demonstrate that TECM delivers speedups of several orders of magnitude over state-of-the-art static baselines, providing a scalable and principled solution for real-time structural analysis in evolving temporal graphs.