Results for “graph-neural-networks”

Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".

DriveLM: Driving with Graph Visual Question Answering

Produces NetworkX code with explicit graph kind, node identity, edge multiplicity, direction, attribute schema, weight semantics, and algorithm preconditions, including testing.

Create, manipulate, and analyze complex networks and graphs using the NetworkX Python package.

Codebase Memory — Knowledge Graph Tools

Search GIF providers with CLI/TUI, download results, and extract stills/sheets.

Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

Creates a New York Times-style data chart frame with animated reveal, supporting line, bar, and range-band charts from CSV/JSON or a textual conclusion.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting...

Write, review, debug, test, or optimize Python code using the rustworkx graph library, with explicit handling of graph kind, index lifecycle, payload semantics, and algorithm result mapping.

Design GraphQL schemas, resolvers, and DataLoader patterns, and integrate clients with Apollo or urql.

Builds a navigable knowledge graph from any folder of files, with community detection, an audit trail, and outputs including interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Simulator.Company graph structure specialist. Use when the user wants to build, edit, analyze, inspect, or ask questions about business process graphs, flowcharts, algorithms, actors (nodes), links (edges), or layers (visual views) in Simulator.Company. Trigger on any of these intents: — Creating: "create graph", "build flowchart", "new diagram", "add actor", "add block to graph", "create algorithm", "draw flowchart", "digital twin", "create process on graph", "build process diagram", "FlowchartBlock", "startStop", "predefinedProcess". — Editing: "edit graph", "update actor", "rename node", "change color", "move actor", "add step to flowchart", "modify diagram", "restructure process", "add edge", "remove link", "reorder steps", "update layer". — Syncing: "push graph", "pull graph", "sync graph", "push changes", "apply edits to layer". — Querying / analysis: "what actors are on this layer", "show me the graph", "who is connected to", "find actor", "list nodes", "describe the process", "analyze the flowchart",

Use when extracting entities and relationships, building ontologies, compressing large graphs, or analyzing knowledge structures - provides structural equivalence-based compression achieving 57-95% size reduction, k-bisimulation summarization, categorical quotient constructions, and metagraph hierarchical modeling with scale-invariant properties. Supports recursive refinement through graph topology metrics including |R|/|E| ratios and automorphism analysis.

Search GIF providers with CLI/TUI, download results, and extract stills/sheets.

Search GIF providers with CLI/TUI, download results, and extract stills/sheets.

Multimodal Neurons in Artificial Neural Networks

Search GIF providers with CLI/TUI, download results, and extract stills/sheets.

GLIP: Grounded Language-Image Pre-training

Foundational Cypher (Neo4j) patterns covering graph pattern matching, MATCH/CREATE/MERGE/DELETE operations, relationships, path patterns, aggregation, filtering, and common graph query patterns. Use when writing Cypher queries, modeling graph data, or needing guidance on graph database operations. This is the entry point for Cypher development.

Knowledge graph and smart memory management using graphify + Obsidian-inspired patterns. Use when: setting up a knowledge graph, managing memory health, cross-linking notes, compiling wiki pages from scattered notes, adding structured frontmatter, or running memory health checks. Triggers on: 'knowledge graph', 'graphify', 'wiki', 'cross-link', 'memory health', 'frontmatter', 'compile notes', 'wikilinks'.

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.