6 Repos
Utilities for importing structured graph data from various formats into deep learning representations.
Distinct from Graph Data Models: Focuses on the loading process into memory for models, rather than the structural data model itself.
Explore 6 awesome GitHub repositories matching data & databases · Graph Data Loaders. Refine with filters or upvote what's useful.
DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks
Imports structured graph data from various source formats into a representation suitable for deep learning models.
Nebula is a distributed graph database designed for storing and querying massive volumes of interconnected vertices and edges across a horizontally scalable cluster. It functions as a Kubernetes-native database and a distributed graph analytics engine, utilizing a Raft-based distributed store to ensure strong consistency and high availability. The system features an OpenCypher query engine for performing complex graph traversals and pattern matching. It distinguishes itself with a decoupled compute-storage architecture and a shared-nothing distributed design, allowing query processing and dat
Provides a utility for reading local CSV files and loading their contents into the graph database.
3d-force-graph is a WebGL-accelerated component for rendering interactive network graphs in three-dimensional space. Built on the ThreeJS library, it positions nodes using a force-directed physics simulation that can be driven by either a D3-force or ngraph engine, producing dynamic layouts that users can rotate, zoom, and pan using mouse, keyboard, or touch controls. The component distinguishes itself through comprehensive camera control capabilities, including programmatic positioning, animated transitions, automatic timed orbiting, and zoom-to-fit functionality that frames all nodes within
Loads graph data from JavaScript objects or JSON sources, defining nodes and links with identifiers.
Apache AGE is a graph database extension for PostgreSQL that adds openCypher graph query capabilities directly within the relational database environment. It functions as a loadable extension that translates Cypher graph traversal queries into SQL expressions, enabling users to run pattern matching and path analysis alongside standard SQL operations within a single database instance. The extension stores labeled, directed property graphs as isolated schemas with internal relational tables for vertices, edges, and labels, preventing cross-graph interference. It supports hybrid query execution
Loads graph data from external file formats into the graph database for querying and analysis.
Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di
Provides high-performance bulk ingestion of CSV data into graph node and relationship tables.
FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut
Provides a high-performance loader for importing large datasets from CSV files into the graph.