Subgraph matching is one of the fundamental problems in graph analytics. Existing methods generate matching orders to guide their search, which consists of a series of extensions. Each time, they extend smaller partial matches into larger ones until all complete answers are obtained. However, these methods have two significant drawbacks. Firstly, their matching order generations are usually heuristic and challenging to be effective for different queries. Secondly, each extension, serving as its computation unit, is coarse-grained and may hinder performance. This granularity issue stems from merging generation and expansion operations into a single computation unit. To address these challenges, we introduce a pioneering framework for Reinforced Subgraph Matching (RSM) that features a fine-grained operation-based search plan. Initially, RSM proposes a fresh paradigm for search, referred to as operation-level search, where each computation unit is defined as an operation that either generates or expands a candidate set under a query vertex. To deal with the second problem and fully exploit the potential of this novel search paradigm, RSM implements a reinforcement learning strategy to generate operation-level search plans. RSM's reinforcement learning approach for constructing operation-based search plans encompasses three modules. In the first module, we employ graph neural networks to extract query vertex representation from graphs. Then, the other two modules leverage multilayer perceptron and are designed to create the generation and expansion operations, respectively. Extensive experiments on real-world graph datasets validate that RSM cuts down query processing time, outperforming existing algorithms by up to 1 to 2 orders of magnitude.

Subgraph query can be applied in various scenarios, such as fraud detection and cyberattack pattern analysis. However, computing subgraph queries usually traverses a huge search space. Many efforts have been made to reduce this search space. The size of the answer set can be exponential, providing a substantial lower bound for the search space. Additionally, different answers may overlap, and a single vertex can occur multiple times in different matches. In this paper, we propose a new problem to compute the match cover of a subgraph query. We define the match cover as a subset of answers such that the vertices included are exactly the same as those in the entire set. There can be more than one match covers, however, we only return one, as long as we can avoid the huge overhead of searching the entire set. It is inefficient to apply traditional subgraph query methods for computing match cover. Specifically, existing methods do not prune partial matches that could grow into full matches. For match cover computation, if the vertices in those full matches are already included in previously found matches, continuing the computation over such partial matches is a waste of time. We propose a new framework, called MatCo, to compute the match cover. In MatCo, we design a new data structure, called local candidate space, to determine whether the future search scopes of partial matches have been covered. We can easily maintain local candidate space and efficiently conduct the determination. We also reduce some Cartesian products, which are inevitable in existing methods, into linear enumerations, which significantly improves performance. Extensive experiments over various datasets confirm that our method outperforms comparative ones by 1~3 orders of magnitude. Efficiently computing the minimum match cover could be an interesting future work.