Collective Memory Graph Analysis

Generated: 2025-05-14 | Snapshot: main | Nodes: 121 | Edges: 248

Executive Summary

The graph exhibits a healthy “hub-and-spoke” topology centered on the Narrative Engine and Collective Memory core concepts, with a high density in system architecture and ontology domains. The primary structural opportunity lies in bridging the gap between technical implementation nodes and the emerging “Human-AI Collaboration” community, which currently lacks strong cross-domain links. The top priority is integrating the isolated User Experience cluster into the core System Design narrative.

Topology Overview

Basic Metrics

| Metric | Value | | :— | :— | | Total Nodes | 121 | | Total Edges | 248 | | Graph Density | 0.017 | | Avg Degree | 4.1 | | Diameter | 7 (approx.) |

Connectivity

The graph consists of one large connected component (92% of nodes) and three small isolated subgraphs (orphans). The largest component is anchored by the narr:Concept and narr:Transaction classes, which serve as the structural backbone for all ingested memories.

Importance Distribution

Hub Concepts (Highest Degree)

Collective Memory (24 connections)
Narrative Engine (19 connections)
Snapshot (15 connections)
Transaction (14 connections)
Ontology (12 connections)
Provenance (11 connections)
Memory File (10 connections)
AI Agent (9 connections)
Git-native (8 connections)
RDF (8 connections)

Bridge Concepts (Highest Betweenness)

Provenance: Connects raw memory files to the compiled snapshot; without this node, the graph splits into “Source” and “State” clusters.
Conviction: Bridges the gap between objective data (Transactions) and subjective weight (Worldview).
Story Template: Connects the technical graph structure to the user-facing output layer.

Core vs. Periphery

The core is dominated by the System Architecture domain (Collective Memory, Snapshot, RDF). The periphery contains specific Implementation Details (e.g., specific SPARQL patterns) and User Personas, which are currently connected by only single edges to the broader graph.

Community Structure

Detected Communities

Cross-Community Links

The System Core and Provenance communities are tightly coupled via the narr:extractedFrom predicate. However, the Narrative/UX community is relatively isolated, connecting to the core primarily through the AI Agent node.

Modularity Assessment

The graph shows high modularity (approx. 0.62), indicating that domains are well-defined and separated. While this provides clarity, the low density of links between Validation and Narrative/UX suggests that the “Conviction” of specific narrative elements is not yet fully mapped.

Coverage Map

By Domain

| Domain | Node Count | Edge Count | Depth Score | | :— | :— | :— | :— | | Architecture | 45 | 112 | 0.85 | | Provenance | 30 | 65 | 0.70 | | Ontology | 20 | 42 | 0.65 | | User Experience | 12 | 18 | 0.35 | | Business/GTM | 14 | 11 | 0.20 |

Dense Areas

The Architecture domain is the most mature, with deep hierarchies for narr:Concept and narr:Transaction. This reflects the project’s early focus on building the technical foundation of the Git-native RDF store.

Sparse Areas

Business/GTM and User Experience are currently “shallow.” Many concepts in these areas (e.g., “Enterprise Adoption,” “Developer Experience”) are mentioned as nodes but lack the rich predicates (like skos:related or narr:impacts) found in the technical domains.

Structural Issues

Orphan Concepts

Legacy Database: Degree 0. (Action: Remove or connect to “Migration Strategy”).
CLI Tooling: Degree 1. (Action: Connect to “Developer Experience”).
Markdown Parser: Degree 1. (Action: Connect to “Memory Ingestion”).

Dead Ends

SPARQL Update: Incoming links from Transactions, but no outgoing links to describe its effect on the Snapshot.
User Interview #4: Incoming provenance link, but no extracted concepts linked back to the graph.

Potential Redundancies

“Memory File” vs “Source Document”: These appear to be used interchangeably in different transactions.
“Confidence Level” vs “Conviction”: The ontology defines Conviction, but some early memories use “Confidence” as a literal value.

Opportunities

High-Value Missing Links

Conviction → Story Template: Linking the weight of knowledge to how it is presented in stories would allow the AI to prioritize “Boulders” over “Notions” in narrative generation.
User Persona → System Core: Connecting specific user needs to architectural components would provide better “Why” context in the graph.

Synthesis Candidates

Merge “Source Document” into “Memory File” to standardize the provenance chain.
Consolidate “Confidence” metrics under the formal “Conviction” ontology class.

Expansion Priorities

User Experience: Expand the “Developer Experience” cluster to match the depth of the “Architecture” domain.
GTM Strategy: Connect the “Enterprise” nodes to the “Security/Permissions” technical nodes.

Temporal Health

Age Distribution

60% of knowledge was ingested in the last 30 days.
25% of knowledge (mostly core ontology) is >90 days old.
15% of knowledge is “stale” (ingested >120 days ago with no updates).

Dormant Areas

The “Validation Logic” domain has not been updated in 45 days, despite recent changes to the “Transaction” structure.

Recent Activity

Heavy growth in the “Provenance” and “Story Generation” domains over the last 14 days, following the integration of the .story template system.

Action Plan

Bridge UX and Core: Create skos:related links between Developer Experience and Memory Ingestion to ground the UX narrative in technical reality.
Resolve Redundancy: Execute a SPARQL refactor to merge “Source Document” into “Memory File” across all historical transactions.
Deepen GTM Domain: Schedule a “Strategy Sync” to extract predicates for the Enterprise Adoption nodes, which are currently sparse.

Citations