arXiv:2608. 02321v1 Announce Type: cross Abstract: Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes.
Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The process of verifying whether a given GFD holds over a particular graph is referred to as GFD validation.
arXiv:2607. 04030v1 Announce Type: cross Abstract: Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data.
arXiv:2607. 10771v1 Announce Type: cross Abstract: Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes.
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.
Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data. We study the CFD discovery problem, which is computationally demanding.
arXiv:2608. 02213v1 Announce Type: cross Abstract: Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references.
arXiv:2512. 10236v2 Announce Type: replace-cross Abstract: Modern ML workloads demand distributing training and inference across multiple GPUs.
The paper introduces a novel LLM‑driven multi‑agent pipeline that converts relational databases into graph databases by standardizing table and column names and iteratively refining the graph schema through ETL, Analyzer, and Graph agents. The resulting graph database meets accuracy, groundedness, and faithfulness criteria and shows significant performance gains, achieving 85.6% Q&A accuracy—12.12% higher than an SQL agent on PostgreSQL—and reducing latency by roughly threefold on a BFSI dataset. This demonstrates an efficient, automated method for transforming tabular data into a more intuitive and faster‑executing graph format.
The paper evaluates seven graph database engines, including Corvic AI, on a synthetic biomedical property graph with 1.02 million nodes and 5.34 million rows. It benchmarks query latency, bulk‑ingest throughput, point‑update latency, and correctness across a twenty‑query workload that covers neighborhood lookups, bounded paths, set intersections, anti‑joins, aggregation, ranking, temporal filters, full scans, and relational joins. The study finds that no single engine is universally fastest; performance depends on query shape, and the largest cost difference arises from bulk‑ingest throughput, which varies by three orders of magnitude and dominates total cost for workloads with fewer than about 10⁵ queries per data refresh.
arXiv:2607. 02603v1 Announce Type: cross Abstract: The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive power of message-passing architectures.
arXiv:2511. 13645v2 Announce Type: replace Abstract: Operational knowledge-graph (KG) pipelines in networking and cybersecurity increasingly need to refresh embeddings under strict time, memory, and audit budgets, especially as curated feeds and LLM-assisted extraction accelerate KG updates.
arXiv:2607. 23632v1 Announce Type: cross Abstract: Science-intensive data profiling focuses on discovery and validation of various patterns in datasets.