NetworkX
NetworkX is an open-source Python library for creating, manipulating, analyzing, and visualizing graphs and networks.
Graph Neural Networks in Action
NetworkX is a Python library for creating, manipulating, analyzing, and visualizing network and graph structures.
- How it is used
- Supports graph exploration, visualization, preprocessing, and handoff to embedding or GNN workflows.
- How deep it goes
- Demonstrates file loading, attributes, component analysis, subgraphs, layouts, and statistics, but defers detailed algorithmic theory.
- Examples
- Uses co-purchase graphs, recruiting social graphs, connected-component extraction, degree plots, histograms, layouts, and adjacency-matrix visualizations.
- What you can do after
- Operate/configure NetworkX to load, inspect, filter, visualize, and prepare graph data for embeddings or GNN training.
Read how Graph Neural Networks in Action explains NetworkX →
Financial AI in Practice
NetworkX is a Python library for creating, manipulating, and studying complex networks.
- How it is used
- Models connected financial activity as graphs to help reveal coordinated fraud.
- How deep it goes
- Builds transaction graphs from tabular data and explores community detection without detailing algorithm internals.
- Examples
- Uses sample e-commerce transaction data to construct a graph and find potentially suspicious clusters.
- What you can do after
- Apply NetworkX to build and explore transaction graphs and detect potentially suspicious communities.
Related concepts
- Dijkstra's algorithmimplements · Dijkstra's algorithm computes shortest paths from a source vertex in a weighted graph with nonnegative edge weights.
- graphuses · A graph is a mathematical structure consisting of vertices connected by edges that represent relationships or connections.
- Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language known for readable syntax and automatic memory management.
- GraphRAGused by · GraphRAG is a retrieval-augmented generation approach that uses graph-structured knowledge and relationships to retrieve context for language-model responses.
- data explorationuses · Data exploration is the systematic examination and summarization of data to understand its structure, quality, distributions, relationships, patterns, and anomalies.
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