Scalable Time-Range k-Core Query on Temporal Graphs(Full Version)
Junyong Yang, Ming Zhong, Yuanyuan Zhu, Tieyun Qian, Mengchi Liu,, Jeffrey Xu Yu

TL;DR
This paper introduces a scalable algorithm for temporal k-core queries on dynamic graphs, significantly reducing redundant computations and enabling efficient, real-time analysis of cohesive subgraphs over time.
Contribution
It proposes the OTCD algorithm with TTI-based pruning and a new in-memory data structure, improving scalability and efficiency for temporal k-core queries without precomputed indexes.
Findings
OTCD outperforms existing methods by three orders of magnitude.
The approach effectively handles dynamic temporal graphs.
Complexity depends only on result size, not time span.
Abstract
Querying cohesive subgraphs on temporal graphs with various time constraints has attracted intensive research interests recently. In this paper, we study a novel Temporal k-Core Query (TCQ) problem: given a time interval, find all distinct k-cores that exist within any subintervals from a temporal graph, which generalizes the previous historical k-core query. This problem is challenging because the number of subintervals increases quadratically to the span of time interval. For that, we propose a novel Temporal Core Decomposition (TCD) algorithm that decrementally induces temporal k-cores from the previously induced ones and thus reduces "intra-core" redundant computation significantly. Then, we introduce an intuitive concept named Tightest Time Interval (TTI) for temporal k-core, and design an optimization technique with theoretical guarantee that leverages TTI as a key to predict…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOpportunistic and Delay-Tolerant Networks · Data Management and Algorithms · Geographic Information Systems Studies
