Results for “graph-classification”

Turns any folder of files into a navigable knowledge graph with community detection, producing interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Analyze git repositories to build a security ownership topology mapping people to files, compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization.

Finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery, generating personalized outreach drafts for email, LinkedIn, and X.

Build interactive, accessible charts, graphs, and data dashboards using Recharts, D3, or Victory. Use when the user says "chart", "graph", "data visualization", "analytics dashboard", "metrics display", "D3", "Recharts", "time-series", or "data display".

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.

Design clear, accessible data visualizations with appropriate chart selection and styling.

Crea gráficos profesionales con Matplotlib, Seaborn y Plotly, desde exploración rápida hasta visualizaciones publicables, eligiendo el tipo de gráfico adecuado para cada historia de datos.

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.

Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.

Converts CSV, Excel, or JSON data into a polished, interactive visual report page with KPI cards, charts, data tables, and insights.

Grades and classifies evaluation batch results, applying exclusions, diagnosing failure modes, computing pass rates, and generating summary tables for papers.