Relationship Mapping v5.0

Correlation Graph

The Correlation Graph maps the hidden relationships between entities. It identifies clusters of adversarial behavior by analyzing trans-transactional links across the entire institutional dataset.

Correlation Logic

quorum-correlation.py

# ENTITY CLUSTERING & GRAPH TRAVERSAL
def map_correlations(event_id, entity_vector):
    # 1. Query Real-Time Graph Store
    graph = GraphDB.connect()
    cluster = graph.find_clusters(entity_vector, depth=3)

    # 2. Identify Shared High-Entropy Signals
    shared_fingerprints = cluster.extract_shared('device_hash', 'ip_asn')
    
    # 3. Calculate Cluster Anomaly Score
    density = len(cluster.nodes) / cluster.temporal_window
    if density > THRESHOLD:
        trigger_sybil_alert(event_id, cluster.id)

    # 4. Append Graph Context to Decision Object
    return {
        "cluster_id": cluster.id,
        "link_count": len(shared_fingerprints),
        "cluster_risk": density
    }

Graph Specs

Sybil Detection

Identifies large-scale botnet activity by detecting high-density clusters of disparate identities sharing identical hardware entropy.

Trans-Session Memory

Maintains state across sessions, allowing the system to remember entity behavior even after credential rotation.

Path Traversal

Analyzes the "distance" between safe identities and known-bad entities within the global risk graph.