Spectral Topology Engine

Analyze directed networks to identify structural holes, hidden dependencies, and missing links via eigenvalue decomposition of combined adjacency and similar...

Spectral Topology Engine — Skill

Overview

Detect structural holes, hidden dependencies, and missing links in directed networks using eigenvalue decomposition of a combined adjacency + similarity matrix.

Location

/home/openclaw/.openclaw/workspace/topology-engine/topology_engine.py

Quick Usage

python

from topology_engine import GraphBuilder, SpectralTopologyAnalyzer, quick_analyze

# From edge list: (source, target, weight)
report = quick_analyze([(0,1,1.0), (1,2,1.0), (2,0,1.0)], n_nodes=3, alpha=0.3)
print(report.summary())

# With metadata similarity
features = np.array([[1,0], [0.9,0.1], [0,1]])
sim = GraphBuilder.cosine_similarity_matrix(features)
adj = GraphBuilder.from_edge_list(3, [(0,1,1.0), (1,2,1.0)])
analyzer = SpectralTopologyAnalyzer(alpha=0.3)
report = analyzer.analyze(adj, similarity=sim)

Key Concepts

• Negative eigenvalues = structural gaps (edges that should exist but don't)
• Alpha = weighting for metadata similarity vs explicit graph structure (default 0.3)
• Spectral gap = difference between largest eigenvalues (measures overall connectivity)
• Cohesion vectors = identify which nodes participate in each gap

Output

• TopologyReport with .summary() for human-readable output
• .to_json(path) for export
• .gaps list of StructuralGap objects with eigenvalue, node_indices, node_weights