Time-Constrained Continuous Subgraph Matching Using Temporal Information for Filtering and Backtracking
CoRR(2023)
Abstract
Real-time analysis of graphs containing temporal information, such as social
media streams, Q&A networks, and cyber data sources, plays an important role in
various applications. Among them, detecting patterns is one of the fundamental
graph analysis problems. In this paper, we study time-constrained continuous
subgraph matching, which detects a pattern with a strict partial order on the
edge set in real-time whenever a temporal data graph changes over time. We
propose a new algorithm based on two novel techniques. First, we introduce a
filtering technique called time-constrained matchable edge that uses temporal
information for filtering with polynomial space. Second, we develop
time-constrained pruning techniques that reduce the search space by pruning
some of the parallel edges in backtracking, utilizing temporal information.
Extensive experiments on real and synthetic datasets show that our approach
outperforms the state-of-the-art algorithm by up to two orders of magnitude in
terms of query processing time.
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Key words
time-constrained continuous sub graph matching,temporal order,time-constrained matchable edge,max-min timestamp,time-constrained pruning
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