neo4j

Existing graph databases lack effective support for the tree-shaped substructures commonly found in property graphs. This work addresses this limitation by treating such tree substructures as first-class citizens and proposes a systematic management framework encompassing modeling, indexing, and query optimization. Drawing inspiration from XML structural indexing techniques, the approach enables efficient path queries within a relational graph database backend. Experimental evaluation demonstrates that the proposed method significantly improves path query performance, thereby validating the potential of structural indexing to enhance graph data management.

Relational databases’ flat data model exhibits impedance mismatch with the nested data structures required by modern applications. This paper introduces the graph-relational database model, which unifies the formal rigor of the relational model with the expressive power of graph-structured nesting to support composable, type-safe complex queries. Our approach comprises three core contributions: (1) EdgeQL—a statically typed, SQL-like query language designed for expressive, safe navigation and transformation of nested, graph-shaped data; (2) the Gel compiler system, which jointly compiles EdgeQL schemas and queries—incorporating both static and dynamic semantics—into highly optimized, native PostgreSQL SQL; and (3) end-to-end type safety without runtime overhead, achieving execution performance comparable to hand-written SQL. Experimental evaluation demonstrates that our system substantially outperforms conventional ORMs, delivering superior balance among query expressivity, developer productivity, and runtime efficiency.

The data complexity of GQL—the standardized query language for graph databases—has long lacked a rigorous theoretical characterization. Method: We establish a unified relational-logic framework for graph queries, embedding full GQL (including arithmetic extensions) into FO[TC] + ESO. We introduce the *Restricted Quantifier Collapse* (RQC), a general technique grounded in finite model theory, and employ register automata modeling coupled with schema validation to analyze query evaluation. Contribution/Results: Our approach yields tight data complexity bounds for GQL and regular path queries (e.g., NL-completeness), and precisely captures regular path queries within FO[TC] while preserving NL complexity. This work provides the first systematic logical foundation for analyzing graph database query complexity, delivering both a unified formal framework and asymptotically optimal, tight complexity characterizations.

Existing graph mining methods primarily focus on topological subgraph discovery and lack a unified mechanism for jointly modeling syntactic and semantic aspects of association rules over attributed graphs. This paper proposes the MINE GRAPH RULE operator, the first to enable integrated syntactic–semantic expression of graph association rules in an attributed graph database—implemented as an extension to Neo4j. Syntactically, conditions are specified via Cypher-like queries; semantically, rule quality is evaluated using support and confidence metrics. The operator tightly couples graph structure with attribute semantics, leverages Neo4j’s native query optimization, and incorporates relational association rule pruning strategies to ensure efficiency and portability. Experiments demonstrate strong scalability across multidimensional parameters. An open-source plugin implementing the operator significantly enhances both the expressiveness and practical utility of graph association rule mining.

Path matching in graph query languages (e.g., Cypher, SQL/PGQ, GQL) lacks a unified and efficient processing mechanism—particularly when supporting complex path semantics (e.g., shortest paths, simple paths) and regular-expression constraints on edge labels—posing dual challenges in expressive power and performance. This paper introduces the first cross-language, general-purpose path-solving framework. It features a compact symbolic path representation and integrates dynamic-programming-based enumeration, incremental pipelined execution, and regex compilation optimizations to enable unified modeling and efficient evaluation of diverse path semantics and edge-label constraints. Experimental evaluation on real-world datasets and complex queries demonstrates an order-of-magnitude speedup over state-of-the-art graph engines, while maintaining high expressiveness, strong scalability, and behavioral stability.

This work addresses the limitation of existing Text2Cypher systems, which are confined to a single graph database and thus unable to support natural language queries across multiple heterogeneous graph databases in real-world scenarios. For the first time, we extend Text2Cypher to a multi-database setting by formally defining the cross-database query reasoning task and its core challenges. We propose a structured three-stage reasoning framework that first routes the query to the relevant target database, then decomposes the original question into sub-queries, and finally performs heterogeneous query reasoning tailored to diverse graph database types and their respective query languages. This framework effectively enables the understanding and execution of cross-database natural language queries, laying a foundation for scalable and practical natural language interfaces to graph databases.

This work addresses the limited expressiveness of existing graph query languages—such as GQL and SQL/PGQ—which lack full compositionality and cannot capture complex path queries within the NLOGSPACE complexity class. To overcome this limitation, the paper introduces a novel query language that unifies graph pattern matching with relational querying through two key innovations: regular path queries enriched with variables and data-value comparisons, and a #Datalog-based graph transformation mechanism capable of constructing nodes, edges, and paths. This combination enables, for the first time, a systematically compositional approach to graph querying that precisely captures the full expressive power of NLOGSPACE. The proposed language not only resolves fundamental expressiveness gaps in current standards but also offers a practical and theoretically grounded extension pathway for both GQL and SQL/PGQ.

GQL (the ISO standard for graph querying) lacks native support for expressing monotonic edge-value constraints along paths, representing a fundamental expressiveness gap in its pattern-matching capabilities. To address this, we propose a constructive graph compilation technique that encodes ordering constraints directly into the graph structure—enabling existing GQL engines to evaluate such queries without syntactic extensions. Our approach integrates structural graph transformation, logical expressibility analysis, and Cypher-specific optimization, and is deeply embedded within the Neo4j runtime. Theoretically, this work closes a long-standing expressiveness gap in GQL concerning path-ordered constraints. Practically, it delivers substantial query performance improvements on real-world workloads, eliminating timeout failures observed with naïve implementations. By bridging formal database theory with industrial graph query engine design, our method advances both the theoretical foundations and practical applicability of standardized graph querying.

This work addresses the longstanding disconnect between visual graph query interfaces (VQIs) and graph query engines (GQEs) by introducing VisualNeo, a novel visual subgraph querying system deeply integrated with Neo4j. VisualNeo bridges the gap between interactive query construction and efficient execution through a data-driven GUI design coupled with predefined query pattern generation, enabling seamless end-to-end subgraph querying and visual exploration of results. By tightly coupling advanced VQI capabilities with the widely adopted Neo4j graph database, VisualNeo significantly lowers the barrier for non-expert users while enhancing both the efficiency and interactivity of subgraph queries over large-scale graph data.

Existing graph database (GDB) models lack hardware–software co-design, resulting in low storage density and inefficient graph traversal—hindering high-performance semantic reasoning required by symbolic AI and retrieval-augmented generation (RAG). To address this, we propose Views, a hardware-accelerated GDB model that redefines the graph data structure through compact symbolic encoding, memory-aligned layout, and hardware-friendly traversal primitives—preserving semantic equivalence while significantly improving storage density and random-access efficiency. Experimental evaluation on representative cognitive modeling and RAG knowledge retrieval tasks demonstrates that Views achieves 2.3×–5.1× higher throughput and reduces latency by 62%–79% compared to state-of-the-art GDBs. Moreover, Views enables scalable symbolic knowledge representation and reasoning, bridging the gap between expressive graph semantics and hardware-efficient execution.