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In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.

Supergraph search is one of fundamental graph query processing problems in many application domains. Given a query graph and a set of data graphs, supergraph search is to find all the data graphs contained in the query graph as subgraphs. In existing algorithms, index construction or filtering approaches are computationally expensive, and search methods can cause redundant computations. In this paper, we introduce four new concepts to address these limitations: (1) DAG integration, (2) dynamic programming between integrated DAG and graph, (3) active-first search, and (4) relevance-size order, which together lead to a much faster and scalable algorithm for supergraph search. Extensive experiments with real datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of indexing time and query processing time.

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