SurrealDB 3.0 wants to replace your five-database RAG stack with one

SurrealDB 3.0 launches with $23M in new funding and a pitch to replace multi-database RAG stacks with a single engine that handles vectors, graphs, and agent memory transactionally.
GitHub

Query your Delta Tables as a Graph

Databricks provides tables designed for massive scale, enabling efficient storage and querying of tens of billions of triples with features like time travel No ETL or migration needed—just query your ...
techxplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

A Comparative Study of Machine Learning and Large Language Models for SQL and NoSQL Injection Vulnerability Detection

The exponential growth of data in relational (SQL) and non-relational (NoSQL) databases has led to an increase in injection attacks, ranking them among the top cybersecurity threats. This study ...
IEEE

Multi-Level Task-Agnostic Graph Representation Learning With Isomorphic-Consistent Variational Graph Auto-Encoders

Abstract: Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice ...
GitHub

bansky-cl/graphrag-arxiv-daily-paper

2023-05-20 Self-Distillation with Meta Learning for Knowledge Graph Completion 2305.12209v1 null 2023-05-17 River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning ...