AlgoGraph v2.0.0 - Advanced Features

AlgoGraph v2.0.0 - Advanced Features

This major release brings AlgoTree-level API elegance to AlgoGraph with pipe-based transformers, declarative selectors, and lazy views—achieving ~90% code reduction for common operations.

Installation

pip install AlgoGraph

What's New

Transformer Pipelines

Compose graph operations using the | pipe operator:

from AlgoGraph.transformers import filter_vertices, largest_component, stats

result = (graph
    | filter_vertices(lambda v: v.get('active'))
    | largest_component()
    | stats())

12 Built-in Transformers:

• filter_vertices, filter_edges - Filter by predicate
• map_vertices, map_edges - Transform attributes
• reverse, to_undirected, subgraph - Structure transformations
• largest_component, minimum_spanning_tree - Algorithm-based
• to_dict, to_adjacency_list, stats - Export operations

Declarative Selectors

Query vertices and edges with logical operators:

from AlgoGraph.graph_selectors import vertex as v, edge as e

# Complex queries with AND, OR, NOT, XOR
power_users = graph.select_vertices(
    v.attrs(active=True) & v.degree(min_degree=10) & ~v.attrs(banned=True)
)

admin_traffic = graph.select_edges(
    e.source(v.attrs(role='admin')) & e.weight(min_weight=100)
)

Selector Types:

• vertex.id(pattern) - Glob/regex matching
• vertex.attrs(**attrs) - Attribute matching with callable support
• vertex.degree(min/max/exact) - Degree-based selection
• edge.weight(), edge.source(), edge.target(), edge.attrs()

Lazy Views

Memory-efficient filtering without copying:

from AlgoGraph.views import filtered_view, neighborhood_view

# Create view without copying
view = filtered_view(large_graph, vertex_filter=lambda v: v.get('active'))

# Iterate lazily
for vertex in view.vertices():
    process(vertex)

# Materialize only when needed
small_graph = view.materialize()

# Explore k-hop neighborhood
local = neighborhood_view(graph, center='Alice', k=2)

6 View Types:

• filtered_view - Filter vertices/edges
• subgraph_view - View specific vertices
• reversed_view - Reverse edge directions
• undirected_view - View as undirected
• component_view - View connected component
• neighborhood_view - k-hop neighborhood

Statistics

• 56+ Algorithms across 8 categories
• 213 Tests passing
• ~2,900 Lines of new code
• Zero Breaking Changes - fully backward compatible with v1.x

Files Added

• transformers.py (660 lines) - Pipe-based transformations
• graph_selectors.py (626 lines) - Declarative pattern matching
• views.py (478 lines) - Lazy graph filtering
• test/test_phase3_features.py (536 lines) - Comprehensive tests
• test/test_phase3_coverage.py (465 lines) - Additional coverage tests

Before/After Comparison

Before (v1.x):

active_verts = graph.find_vertices(lambda v: v.get('active') and graph.degree(v.id) >= 5)
subg = graph.subgraph({v.id for v in active_verts})
components = connected_components(subg)
largest = max(components, key=len)
comp_graph = subg.subgraph(largest)
stats = {'vertices': comp_graph.vertex_count, 'edges': comp_graph.edge_count}

After (v2.0.0):

from AlgoGraph.transformers import filter_vertices, largest_component, stats

result = (graph
    | filter_vertices(lambda v: v.get('active') and graph.degree(v.id) >= 5)
    | largest_component()
    | stats())

Links

• PyPI: https://pypi.org/project/AlgoGraph/2.0.0/
• Documentation: https://queelius.github.io/AlgoGraph/
• Repository: https://github.com/queelius/AlgoGraph