While large language models have revolutionized Text-to-SQL tasks, translating natural language into Graph Query Languages (Text2GQL) remains underexplored due to the topological heterogeneity and syntactic diversity of graph query languages (e.g., Cypher, Gremlin, SPARQL).Existing approaches often struggle with structural hallucinations and lack adaptability in cold-start scenarios.In this paper, we present a unified, trainingfree Text2GQL framework.First, Structural Twig Linking elevates schema grounding to the identification of semantic substructures ("twigs"), providing robust topological priors.Second, addressing data scarcity, Evolutionary In-Context Learning operates in a Tabula Rasa setting to implicitly construct a selfgrowing repository of verified examples driven by syntactic utility.Finally, our Adversarial Execution-Guided Correction agent enforces fidelity through synergistic static critique and dynamic verification.Experiments demonstrate significant improvements over baselines in both accuracy and executability across diverse GQLs.The code is available at https: //github.com/nf202/Text2Graph.

Graph database systems play a vital role in graph structure analysis across a wide range of application domains. Queries with set-level constraints on community structures are increasingly demanded in real-world applications. However, existing graph databases lack native support for both efficient monadic second-order logic (MSOL) query processing and fast community retrieval, hindering their applicability to such analytical tasks. In this paper, we present Monac- Graph, a graph database system that enables practical MSOL queries. MonacGraph features an efficient two-phase execution engine that minimizes redundant first-order clause evaluations. We propose SO-Gremlin, an extension of the Gremlin graph traversal language with intuitive syntax for set quantification. The system adopts LSM-Community as its storage backend, enabling efficient queries over precomputed graph structures. Additionally, MonacGraph provides a user-friendlyWeb interface for composing complex set-level queries and visualizing results in real time. A demonstration video can be found at https://www.youtube.com/watch?v=Eezdq9tzbJE.

Subgraph matching is a fundamental operation in graph data management and facilitates the analysis of complex datasets in various domains such as computer vision, bioinformatics, and social networks. With the scale of graph data getting larger and larger, the performance of subgraph matching algorithms is facing significant challenges. However, existing tools are not efficient enough for subgraph matching tasks. In this paper, we present a subgraph matching tool called RAISIN. To improve the efficiency of subgraph matching tasks, RAISIN accelerates the algorithms of the matching engine with community-structure-based and parallelism optimization. Moreover, to improve user-friendliness, RAISIN provides a graphical user interface to monitor the matching process and visualize matching results. Finally, three use cases are presented to demonstrate the efficiency and user-friendliness of RAISIN.

Recently, several social network analysis algorithms have been optimized by leveraging the community structure commonly found in graphs. Since community structure is fun-damental to these algorithms, storing graphs based on their community structure can significantly enhance the performance of graph algorithms that rely on it for optimization. However, existing graph storage systems do not natively store graphs according to the community structure, which limits their per-formance in retrieving communities. To fill this gap, we pro-pose LSM-Community, a graph storage system inspired by the LSM - Tree design that stores graphs on disk based on their community structure. To dynamically maintain the community structure during graph updates, we present the community-centric dynamic community detection algorithm$(C^{3}D)$. Experimental results demonstrate that LSM-Community outperforms other storage systems in classical community discovery tasks (e.g., performing CD on UK-2007 dataset with LSM-Community is$86.12\times$faster than Neo4j) while maintaining high performance on classical graph analytic algorithms. This indicates that LSM-Community efficiently supports community discovery and query processing while preserving the performance of classical analytic algorithms,

Procedural Language extensions of SQL (abbr. PL/SQL) enhance database programming by integrating procedural constructs with SQL's declarative syntax, thereby improving the reusability, modularity, and maintainability of SQL. Besides, PL/SQL in database systems presents significant challenges in real-world development, primarily due to the inherent complexity of programming. To reduce the development difficulty of PL/SQL, this paper studies the novel task of translating natural language (NL) to PL/SQL (i.e., NL-to-PL/SQL), aimed at simplifying PL/SQL development. Recent advancements in language models have shown promise in translating natural language questions into SQL queries (i.e., Text-to-SQL). However, the state-of-the-art Text-to-SQL methods focus only on single SQL queries, neglecting the procedural extensions of SQL, which limits their effectiveness for the NL-to-PL/SQL task. In this paper, we propose PLForge, a suite of pre-trained language models with parameter configurations of 3B, 7B, and 15B, tailored for NL-to-PL/SQL tasks. To enhance the PL/SQL generation capabilities of PLForge, we leverage a curated PL/SQL-centric data corpus and employ an incremental pre-training approach. Furthermore, to fully exploit the potential of PLForge, we propose a comprehensive prompt construction strategy tailored specifically for PL/SQL. Given the scarcity of NL-to-PL/SQL datasets, we develop a template-based method for generating NL-to-PL/SQL data. We conduct a series of experiments on PLForge and several baseline models. Based on execution match and exact match metrics that are designed specifically for the NL-to-PL/SQL task, the experimental results demonstrate that PLForge outperforms existing models in both in-context learning and supervised fine-tuning settings.

With the wide application of graphs in various fields, graph query languages have attracted more and more attention. Existing graph query languages, such as GraphQL and SoQL, mostly have similar expressive power as the first-order logic or its extended versions, and are limited when used to express various queries. In this paper, since the graph data model is the base of the graph query language, we propose a new graph data model with the expressive power of monadic second-order logic (abbr. MSOL), and then present a more expressive SQL-like declarative graph query language named$SOGQL$to support more common queries efficiently. Specifically, a new graph calculus is first proposed based on MSOL for attributed graphs. Then, the new graph data model is proposed. Its graph algebra, which operates on graph sets, has seven fundamental operators such as union, filter, map, and reduce. Next, the graph query language$SOGQL$is proposed based on the graph data model. Since the graph algebra has the same expressive power as the graph calculus,$SOGQL$has the expressive power of MSOL, and can express queries with constraints on subgraphs. Moreover, applied with$SOGQL$, a prototype system named$SOGDB$is implemented.$SOGDB$is applied with$SOGQL$, and the experimental results show its efficiency.