Documentation Index
Fetch the complete documentation index at: https://docs.emergent.community/llms.txt
Use this file to discover all available pages before exploring further.
Overview
In Emergent, agents live on the nodes of a graph. Edges define which agents can interact. The topology you choose has a major impact on how information, behavior, or state propagates through your simulation.
Emergent ships with three built-in topologies and supports any custom NetworkX graph.
Built-in topologies
Set graph_type in your parameters before calling initialize_graph().
Complete graph
model.update_parameters({"graph_type": "complete", "num_nodes": 6})
Cycle graph
model.update_parameters({"graph_type": "cycle", "num_nodes": 6})
Wheel graph
model.update_parameters({"graph_type": "wheel", "num_nodes": 6})
Custom graphs
For real-world or research-grade topologies, provide your own NetworkX graph:
import networkx as nx
from emergent import AgentModel
model = AgentModel()
# Scale-free network (Barabási–Albert)
G = nx.barabasi_albert_graph(n=200, m=3)
model.set_graph(G)
model.set_initial_data_function(my_initial_data)
model.initialize_graph()
nx.Graph (undirected) is accepted. Common choices for ABM research:
| Graph type | NetworkX constructor | Use case |
|---|
| Barabási–Albert | nx.barabasi_albert_graph(n, m) | Scale-free / social networks |
| Erdős–Rényi | nx.erdos_renyi_graph(n, p) | Random networks |
| Watts–Strogatz | nx.watts_strogatz_graph(n, k, p) | Small-world networks |
| Grid | nx.grid_2d_graph(m, n) | Spatial / lattice models |
| Karate club | nx.karate_club_graph() | Classic social network dataset |
When modeling a real social or organizational network, you can load an edge list or adjacency matrix and construct the graph with nx.from_edgelist() or nx.from_numpy_array().
Accessing graph data
After initialization, retrieve the graph and inspect or manipulate node data directly:
graph = model.get_graph()
# Iterate over all nodes and their data
for node, data in graph.nodes(data=True):
print(node, data)
# Access a specific node's data
print(graph.nodes[0])
# Get neighbors of a node
neighbors = list(graph.neighbors(0))
Emergent does not copy the graph when you call get_graph(). Mutations you make to the returned graph object are reflected in the model’s internal state immediately.
