NetworkX
Package: networkx · 20 nodes · Graph analysis and network science
Build graph analysis pipelines visually — create graphs, run algorithms, and inspect results on a canvas. NetworkX can run as a standalone project or be enabled as a composable library inside any framework's graph.
Node Reference
| Node | Type | Inputs | Outputs |
|---|---|---|---|
| Create Graph | statement | - | Graph (nx.Graph) |
| Add Node | statement | Graph (nx.Graph), Node (any) | - |
| Add Edge | statement | Graph (nx.Graph), From (any), To (any), Weight (float) | - |
| Has Node | expression | Graph (nx.Graph), Node (any) | Exists (bool) |
| Has Edge | expression | Graph (nx.Graph), From (any), To (any) | Exists (bool) |
| Neighbors | expression | Graph (nx.Graph), Node (any) | Neighbors (list |
| Degree | expression | Graph (nx.Graph), Node (any) | Degree (int) |
| Node Count | expression | Graph (nx.Graph) | Count (int) |
| Complete Graph | expression | Nodes (int) | Graph (nx.Graph) |
| Random Graph | expression | Nodes (int), Probability (float) | Graph (nx.Graph) |
| Barabasi Albert | expression | Nodes (int), Edges/Node (int) | Graph (nx.Graph) |
| Grid 2d | expression | Rows (int), Cols (int) | Graph (nx.Graph) |
| Shortest Path | expression | Graph (nx.Graph), Source (any), Target (any) | Path (list |
| Shortest Path Length | expression | Graph (nx.Graph), Source (any), Target (any) | Length (int) |
| Has Path | expression | Graph (nx.Graph), Source (any), Target (any) | Connected (bool) |
| Degree Centrality | expression | Graph (nx.Graph) | Centrality (dict<string, any>) |
| Betweenness Centrality | expression | Graph (nx.Graph) | Centrality (dict<string, any>) |
| Pagerank | expression | Graph (nx.Graph) | Ranks (dict<string, any>) |
| Connected Components | expression | Graph (nx.Graph) | Components (list |
| Is Connected | expression | Graph (nx.Graph) | Connected (bool) |
Creating Graphs
Create Graph is a statement node. Use the dropdown to select the graph type:
| Type | Directed | Parallel Edges | Python |
|---|---|---|---|
| Graph | No | No | nx.Graph() |
| DiGraph | Yes | No | nx.DiGraph() |
| MultiGraph | No | Yes | nx.MultiGraph() |
| MultiDiGraph | Yes | Yes | nx.MultiDiGraph() |
Add Node generates G.add_node(n). Add Edge generates G.add_edge(u, v) or G.add_edge(u, v, weight=w) when weight is connected.
Graph Generators
Generator nodes are expression nodes — no exec ports, they produce a graph value directly.
- Complete Graph — Kn:
nx.complete_graph(n) - Random Graph — Erdos-Renyi:
nx.erdos_renyi_graph(n, p) - Barabasi-Albert — Scale-free via preferential attachment:
nx.barabasi_albert_graph(n, m) - Grid 2D — Lattice:
nx.grid_2d_graph(m, n)
Querying Graphs
All query nodes are expression nodes resolved when their output is needed.
- Has Node / Has Edge — Boolean existence checks
- Neighbors —
list(G.neighbors(n)) - Degree —
G.degree(n) - Node Count —
G.number_of_nodes()
Paths & Distances
- Shortest Path —
nx.shortest_path(G, source, target)(raises error if no path) - Shortest Path Length —
nx.shortest_path_length(G, source, target) - Has Path —
nx.has_path(G, source, target)(use with Branch for safe path-finding)
Centrality Measures
All return a dict mapping node IDs to float scores.
- Degree Centrality — Importance by connection count, normalized [0, 1]
- Betweenness Centrality — How often a node is a bridge between others
- PageRank — Recursive importance (works best on DiGraph)
Connected Components
- Connected Components — Returns Components (list of sets) and Count
- Is Connected — Boolean check for full connectivity
These work on undirected graphs. For directed graphs, Is Connected performs a weak connectivity check.