Results for “graphs”

Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about `graphsignal-run` or `graphsignal.watch()`, or asks about CUPTI / Prometheus / OTLP setup.

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Create short animated explainers with Manim for technical concepts, graphs, system diagrams, and product walkthroughs. Plan scenes, render a video, and prepare a thumbnail, with optional handoff for further editing or compositing.

• Explain a technical concept with animation
• Visualize changes in graphs or network diagrams
• Show a workflow or system architecture

Build a graph of functions and calls across multiple programming languages to investigate entry points, paths to sensitive operations, and potential impact. Use the results to prioritize security reviews and connect external findings with source code.

• Tracing call paths from entry points to sensitive functions
• Prioritizing code for a security audit
• Connecting calls across language and RPC boundaries

Create Mermaid diagrams from code relationships parsed by Trailmark. Visualize function calls, class inheritance, module dependencies, class members, complexity hotspots, and paths from inputs to sensitive functions, with adjustable focus, depth, and layout.

• Mapping function calls around a selected function
• Visualizing class inheritance and members
• Drawing module dependency maps

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

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PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.

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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

The English summary is being prepared. Showing the original description.

Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

The English summary is being prepared. Showing the original description.

Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.

The English summary is being prepared. Showing the original description.

Search and edit notes in a SiYuan knowledge base through its API. Find text, read individual content blocks, create documents, update paragraphs, rename or delete notes, and export documents as Markdown.

• Search existing notes by keyword
• Save meeting notes as a new document
• Append or update paragraphs in a note

Create HTML presentations for marketing, pitches, and data-driven explanations. Use Chart.js graphs, reusable design settings, responsive layouts, and writing formulas to shape slide content and choose layouts suited to the presentation's purpose.

• Creating marketing presentation slides
• Building a pitch deck
• Explaining data with Chart.js graphs

Create mathematical animations in ManimGL using Python, with guidance for equations, graphs, 3D scenes, and camera movement. Includes interactive development workflows, examples, and templates for testing changes and rendering scenes to video.

• Animate equations and mathematical derivations
• Visualize functions and graphs
• Build 3D scenes with camera movement

Create mathematical and geometric animations in Python with Manim Community Edition. Use guidance, examples, and scene templates to structure animations, display equations, plot graphs, control cameras, and render two-dimensional or three-dimensional scenes.

• Animating equations and derivations
• Creating function graphs and area visualizations
• Building 3D surfaces with camera movement

Choose tools and workflows for 3D modeling, dimensioned CAD parts, scan processing, rendering, and fabrication. Capability tables and recipes help connect tasks from photographs or initial designs to printable parts, CNC toolpaths, or game assets.

• Plan a workflow from dimensioned CAD to 3D printing
• Model and export a game asset
• Reconstruct a 3D model from photographs

Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.

• Planning tile extraction from whole-slide images
• Reviewing preprocessing and image quality
• Validating multiplex measurements and spatial graphs

Create or edit Markdown with each prose paragraph on one source line, letting the viewer wrap the text automatically. Preserve meaningful line breaks in lists, code, and poetry while reducing unnecessary changes in file comparisons.

• Writing READMEs without fixed-width line wrapping
• Joining manually wrapped Markdown paragraphs
• Keeping Markdown changes easier to review

Turn notes, prose, code, schemas, or tables into editable diagrams, including flowcharts, architecture diagrams, and schedules. Choose Mermaid, Graphviz DOT, PlantUML, or SVG to suit the content, with image or PDF rendering when needed.

• Turn business procedures into flowcharts
• Diagram system architecture and dependencies
• Visualize database relationships and interactions

Draft a LinkedIn post from a topic or rough notes, matching the tone and length of past posts. Shape one clear idea into a specific opening, short paragraphs, and a gentle discussion prompt, ready for review.

• Turn rough notes into a LinkedIn draft
• Match the tone and length of past posts
• Share an experience or lesson in short paragraphs

Build and search a project knowledge graph, a map of information and its relationships. Check matching entries, relationship confidence, graph freshness, and changes since the previous build to understand the project's recorded structure.

• Building a map of project information and relationships
• Searching for a term and its related entries
• Checking whether the graph reflects recent project changes

Create static technical drawings from object descriptions, photographs, or CAD models. Produce consistent views that explain shape or assembly, plus a viewer with zoom, editable colors, and downloads in SVG, PNG, and PDF formats.

• Creating an exploded view to explain an assembly
• Drawing aligned views from supplied dimensions
• Illustrating an object's shape from photographs

Read or create DOCX documents when standard file tools fail. This fallback extracts text from the document's internal XML and uses python-docx through shell commands to build new files with headings, paragraphs, and tables.

• Extract text from a DOCX file that standard tools cannot read
• Create a Word document after sandbox execution fails
• Build a business memo with headings and tables

Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.

• Connecting Semgrep or CodeQL results to code structure
• Combining human audit notes with automated findings
• Prioritizing findings using input flow and impact