adaptive-graph-engine

Adaptive Graph Engine

Adaptive Graph Engine is a Python graph engine for modelling and analysing directed dependency networks. It provides path discovery, BFS/DFS traversal, weighted shortest paths, cycle detection, topological ordering, structural graph analysis, persistence, and change tracking across evolving graph states.

Features

• Directed graph representation
• Nodes and edges
• Parallel edges
• Edge weights
• Node metadata
• Relationship metadata
• Neighbour lookup
• Predecessor and successor lookup
• Node and edge counts
• In-degree, out-degree and total degree
• Breadth-First Search (BFS)
• Depth-First Search (DFS)
• Dijkstra shortest weighted path
• Directed cycle detection
• Topological sorting
• Connected components
• Local neighbourhood analysis
• Dictionary serialization and restoration
• JSON save and load
• Graph state comparison
• Node and edge change detection
• Edge weight change detection

Installation

pip install adaptive-graph-engine

Basic Usage

from adaptive_graph_engine import Graph

graph = Graph()

graph.add_edge(
    "salesforce",
    "customer_raw",
    metadata={"relationship": "INGESTS_TO"}
)

graph.add_edge(
    "customer_raw",
    "customer_clean",
    metadata={"relationship": "TRANSFORMS_TO"}
)

print(graph.get_neighbors("salesforce"))

Output:

['customer_raw']

Graph Algorithms

Algorithms are available from adaptive_graph_engine.algorithms.

Breadth-First Search

Find a path with the fewest hops.

from adaptive_graph_engine.algorithms import find_shortest_path

path = find_shortest_path(
    graph,
    "salesforce",
    "customer_clean"
)

Depth-First Search

Explore a graph using depth-first traversal.

from adaptive_graph_engine.algorithms import find_depth_first

path = find_depth_first(
    graph,
    "salesforce",
    "customer_clean"
)

Dijkstra

Find the lowest-cost path using edge weights.

from adaptive_graph_engine.algorithms import dijkstra

result = dijkstra(
    graph,
    "salesforce",
    "customer_clean"
)

Dijkstra returns the path and its total cost.

Cycle Detection

from adaptive_graph_engine.algorithms import has_cycle

contains_cycle = has_cycle(graph)

Topological Sort

from adaptive_graph_engine.algorithms import topological_sort

order = topological_sort(graph)

Topological sorting returns None when the directed graph contains a cycle.

Connected Components

Find groups of connected nodes. Direction is ignored when determining component membership.

from adaptive_graph_engine.algorithms import get_connected_components

components = get_connected_components(graph)

Local Neighbourhood

Inspect the graph surrounding a node up to a specified depth.

from adaptive_graph_engine.algorithms import get_neighborhood

neighborhood = get_neighborhood(
    graph,
    "customer_raw",
    depth=2
)

Graph Statistics

The graph provides basic structural measurements.

graph.number_of_nodes()
graph.number_of_edges()

graph.in_degree("customer_raw")
graph.out_degree("customer_raw")
graph.degree("customer_raw")

Parallel edges are counted separately.

Graph Persistence

Graphs can be converted to dictionaries and reconstructed later.

data = graph.to_dictionary()

restored_graph = Graph.from_dictionary(data)

Graph state can also be saved to and loaded from JSON.

from adaptive_graph_engine.io import save_json, load_json

save_json(
    graph,
    "graph.json"
)

restored_graph = load_json(
    "graph.json"
)

Serialization preserves node metadata, edge weights, relationship metadata and parallel edges.

Graph Change Tracking

Adaptive Graph Engine can compare two graph states and report structural changes.

from adaptive_graph_engine import Graph
from adaptive_graph_engine.changes import compare_graphs

old_graph = Graph()

old_graph.add_edge(
    "customer_clean",
    "revenue_model",
    weight=2,
    metadata={
        "relationship": "FEEDS"
    }
)

new_graph = Graph()

new_graph.add_edge(
    "customer_clean",
    "revenue_model",
    weight=8,
    metadata={
        "relationship": "FEEDS"
    }
)

changes = compare_graphs(
    old_graph,
    new_graph
)

print(changes)

Graph comparison can detect:

• Nodes added
• Nodes removed
• Edges added
• Edges removed
• Edge weight changes
• Changes across parallel relationships

Change tracking provides a foundation for systems that need to observe how dependency and relationship structures evolve over time.

Example Use Cases

Adaptive Graph Engine can be used as a foundation for:

• Dependency graphs
• Data lineage
• Provenance graphs
• Workflow dependencies
• Knowledge graph infrastructure
• Relationship-aware systems
• Graph change monitoring
• Evolving dependency networks

Requirements

Python 3.11 or later.

Version

Current release: 0.3.0

Author

Yassine Chaachaa

License

MIT