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title Anomaly Detection Report
generated 2025-12-13
model_version v3.1.0
dataset AxonFramework-4.12.2
authors
JohT/code-graph-analysis-pipeline

📊 Anomaly Detection Report

1. Executive Overview

This report analyzes structural and dependency anomalies across multiple abstraction levels of the codebase. The goal is to detect potential software quality, design, and architecture issues using graph-based features, anomaly detection (Isolation Forest), and SHAP explainability.

📚 Table of Contents

  1. Executive Overview
  2. Deep Dives by Abstraction Level
  3. Plot Interpretation Guide
  4. Taxonomy of Anomaly Archetypes
  5. Recommendations
  6. Appendix

1.1 Anomalies in total

Analyzed Units Anomalies Authorities Bottlenecks Bridges Hubs Outliers
1650 81 22 21 16 11 9

1.2 Overview of Analyzed Structures

Abstraction Level Units Anomalies Authorities Bottlenecks Bridges Hubs Outliers
Type,Java,Class 1101 39 9 4 8 3 1
Type,Java,Interface 264 31 1 6 1 2 1
Package,Java 146 6 10 10 6 5 7
Type,Java,Enum 30 4 0 0 1 0 0
Type,Java,Class,Throwable 56 1 0 0 0 0 0
Type,Java,Annotation 44 0 0 0 0 0 0
Artifact,Jar,Archive,Zip,Java 9 0 2 1 0 1 0

1.3 Overview Charts

Treemap Charts

JavaTreemap1AverageAnomalyScorePerDirectory

JavaTreemap2ArchetypesOverviewPerDirectory

JavaTreemap3ArchetypeAuthorityPerDirectory

JavaTreemap4ArchetypeBottleneckPerDirectory

JavaTreemap5ArchetypeBridgePerDirectory

JavaTreemap6ArchetypeHubPerDirectory

JavaTreemap7ArchetypeOutlierPerDirectory

2. Deep Dives by Abstraction Level

Each abstraction level includes anomaly statistics, SHAP feature importance, archetype distribution, and example anomalies.

2.1 Java Artifact

Anomaly Results

Total anomalies
Anomalies Authorities Bottlenecks Bridges Hubs Outliers
0 2 1 0 1 0
Top global contributing features (via SHAP)

⚠️ No anomaly detection and SHAP data available for this level (model skipped or insufficient samples).

Archetype Distribution

Archetype Count Max. Score Model Status Examples
Authority 2 null Undetermined /axon-messaging-4.12.2.jar, /axon-spring-boot-autoconfigure-4.12.2.jar
Bottleneck 1 null Undetermined /axon-disruptor-4.12.2.jar
Hub 1 null Undetermined /axon-messaging-4.12.2.jar

Top anomalies with their local contributing features (via SHAP)

⚠️ No anomaly detection and SHAP data available for this level (model skipped or insufficient samples).

Visualizations

See Plot Interpretation Guide on how to read the plots in detail.

⚠️ No anomaly detection and SHAP data available for this level (model skipped or insufficient samples).

Graph Visualizations

TopHub Graph Visualizations

TopHub 1

TopBottleneck Graph Visualizations

TopBottleneck 1

TopAuthority Graph Visualizations

TopAuthority 1

TopAuthority 2

--

2.2 Java Package

Anomaly Results

Total anomalies
Anomalies Authorities Bottlenecks Bridges Hubs Outliers
6 10 10 6 5 7
Top global contributing features (via SHAP)
Feature Mean absolute SHAP value
Node embeddings aggregated 0.031674
betweenness 0.016335
incomingDependencies 0.015197
articleRank 0.008969
degree 0.008001
pageToArticleRankDifference 0.007811
pageRank 0.007810
localClusteringCoefficient 0.007450
outgoingDependencies 0.004854
nodeEmbeddingPCA_12 0.004121
nodeEmbeddingPCA_14 0.003720

Archetype Distribution

Archetype Count Max. Score Model Status Examples
Authority 1 0.0023 Anomalous org.axonframework.common
Bottleneck 4 0.0526 Anomalous org.axonframework.messaging, org.axonframework.eventhandling, org.axonframework.serialization
Bridge 6 0.0526 Anomalous org.axonframework.messaging, org.axonframework.eventhandling, org.axonframework.eventsourcing.eventstore.jdbc
Hub 4 0.0526 Anomalous org.axonframework.messaging, org.axonframework.eventhandling, org.axonframework.serialization
Authority 9 -0.017 Typical org.axonframework.axonserver.connector, org.axonframework.common.transaction, org.axonframework.deadline.annotation
Bottleneck 6 -0.0057 Typical org.axonframework.commandhandling, org.axonframework.eventsourcing.eventstore, org.axonframework.messaging.annotation
Hub 1 -0.0701 Typical org.axonframework.springboot.autoconfig
Outlier 7 -0.0418 Typical org.axonframework.eventhandling.deadletter.jdbc, org.axonframework.springboot.autoconfig.legacyjpa, org.axonframework.queryhandling.annotation

Top anomalies with their local contributing features (via SHAP)

Name Contained in Anomaly Score Archetypes Top Feature 1 Top Feature 1 SHAP Top Feature 2 Top Feature 2 SHAP Top Feature 3 Top Feature 3 SHAP Model Status
org.axonframework.messaging axon-messaging-4.12.2 0.0526 Bottleneck, Hub, Bridge betweenness -0.2102 incomingDependencies -0.1386 pageToArticleRankDifference -0.1092 Anomalous
org.axonframework.eventhandling axon-messaging-4.12.2 0.0331 Bottleneck, Hub, Bridge betweenness -0.2221 incomingDependencies -0.1388 articleRank -0.0996 Anomalous
org.axonframework.eventsourcing.eventstore.jdbc axon-eventsourcing-4.12.2 0.0266 Bridge nodeEmbeddingPCA_10 -0.0789 outgoingDependencies -0.0701 nodeEmbeddingPCA_14 -0.0698 Anomalous
org.axonframework.serialization axon-messaging-4.12.2 0.0252 Bottleneck, Hub, Bridge betweenness -0.2233 incomingDependencies -0.1307 articleRank -0.1049 Anomalous
org.axonframework.config axon-configuration-4.12.2 0.0148 Bridge, Bottleneck, Hub betweenness -0.1958 degree -0.1331 outgoingDependencies -0.0717 Anomalous
org.axonframework.common axon-messaging-4.12.2 0.0023 Authority, Bridge pageToArticleRankDifference -0.154 articleRank -0.1294 incomingDependencies -0.1255 Anomalous

Visualizations

See Plot Interpretation Guide on how to read the plots in detail.

Anomalies

Anomalies

Global feature importance SHAP summary plots

Anomaly feature importance explained (global)

Feature dependence plots for top important features

Anomaly feature dependence explained (global)

Local SHAP Force Plots – Top 6 Anomalies

Top 1 anomaly - local feature importance Top 2 anomaly - local feature importance Top 3 anomaly - local feature importance Top 4 anomaly - local feature importance Top 5 anomaly - local feature importance Top 6 anomaly - local feature importance

Cluster Diagnostics

Cluster Overall

Cluster Membership Strength

Cluster probabilities

Cluster Noise and Bridge Analysis

Cluster Noise: Highly central and popular Cluster Noise: Poorly integrated bridges Cluster Noise: Role inverted bridges

Feature Distributions

Betweenness Centrality Distribution Clustering coefficient distribution PageRank minus ArticleRank distribution

Feature Relationships

Clustering coefficient versus PageRank

Graph Visualizations

TopHub Graph Visualizations

TopHub 1

TopHub 2

TopHub 3

TopHub 4

TopHub 5

TopBottleneck Graph Visualizations

TopBottleneck 1

TopBottleneck 2

TopBottleneck 3

TopBottleneck 4

TopBottleneck 5

TopAuthority Graph Visualizations

TopAuthority 1

TopAuthority 2

TopAuthority 3

TopAuthority 4

TopAuthority 5

TopBridge Graph Visualizations

TopBridge 1

TopBridge 2

TopBridge 3

TopBridge 4

TopBridge 5

TopOutlier Graph Visualizations

TopOutlier 1

TopOutlier 2

TopOutlier 3

TopOutlier 4

TopOutlier 5

--

2.3 Java Type

Anomaly Results

Total anomalies
Anomalies Authorities Bottlenecks Bridges Hubs Outliers
75 10 10 10 5 2
Top global contributing features (via SHAP)
Feature Mean absolute SHAP value
Node embeddings aggregated 0.040811
articleRank 0.013365
pageRank 0.012756
pageToArticleRankDifference 0.010716
degree 0.008191
incomingDependencies 0.006468
betweenness 0.005789
nodeEmbeddingPCA_28 0.003720
nodeEmbeddingPCA_27 0.002777
nodeEmbeddingPCA_22 0.002738
nodeEmbeddingPCA_5 0.002297

Archetype Distribution

Archetype Count Max. Score Model Status Examples
Authority 7 0.0978 Anomalous org.axonframework.serialization.SerializedType, org.axonframework.messaging.MetaData, org.axonframework.serialization.SimpleSerializedType
Bottleneck 5 0.1071 Anomalous org.axonframework.messaging.Message, org.axonframework.springboot.autoconfig.AxonAutoConfiguration, org.axonframework.messaging.unitofwork.UnitOfWork
Bridge 10 0.0183 Anomalous org.axonframework.commandhandling.distributed.commandfilter.DenyCommandNameFilter, org.axonframework.modelling.saga.repository.AssociationValueMap$1, org.axonframework.commandhandling.distributed.commandfilter.OrCommandMessageFilter
Hub 5 0.1071 Anomalous org.axonframework.messaging.Message, org.axonframework.eventhandling.EventMessage, org.axonframework.common.BuilderUtils
Authority 3 -0.0143 Typical org.axonframework.eventsourcing.eventstore.jdbc.EventSchema$Builder, org.axonframework.eventhandling.tokenstore.jdbc.TokenSchema, org.axonframework.common.TypeReflectionUtils$VarMap
Bottleneck 5 -0.0191 Typical org.axonframework.messaging.GenericMessage, org.axonframework.config.EventProcessingModule, org.axonframework.config.Configurer
Outlier 2 -0.023 Typical org.axonframework.springboot.TracingProperties, org.axonframework.eventsourcing.eventstore.jdbc.EventTableFactory

Top anomalies with their local contributing features (via SHAP)

Name Contained in Anomaly Score Archetypes Top Feature 1 Top Feature 1 SHAP Top Feature 2 Top Feature 2 SHAP Top Feature 3 Top Feature 3 SHAP Model Status
org.axonframework.messaging.Message axon-messaging-4.12.2 0.1071 Hub, Bottleneck articleRank -0.2059 pageRank -0.2053 degree -0.1629 Anomalous
org.axonframework.serialization.SerializedType axon-messaging-4.12.2 0.0978 Authority articleRank -0.2168 pageRank -0.2104 pageToArticleRankDifference -0.1537 Anomalous
org.axonframework.messaging.MetaData axon-messaging-4.12.2 0.0869 Authority articleRank -0.2059 pageRank -0.2019 degree -0.162 Anomalous
org.axonframework.serialization.SerializedObject axon-messaging-4.12.2 0.0803 articleRank -0.2082 pageRank -0.1997 degree -0.1623 Anomalous
org.axonframework.serialization.Serializer axon-messaging-4.12.2 0.0801 articleRank -0.2138 pageRank -0.2005 degree -0.1567 Anomalous
org.axonframework.eventhandling.EventMessage axon-messaging-4.12.2 0.0682 Hub articleRank -0.2031 pageRank -0.1966 degree -0.1849 Anomalous
org.axonframework.serialization.SimpleSerializedType axon-messaging-4.12.2 0.0619 Authority pageRank -0.2673 pageToArticleRankDifference -0.2161 articleRank -0.1454 Anomalous
org.axonframework.common.Assert axon-messaging-4.12.2 0.0593 Authority pageRank -0.2118 articleRank -0.2101 degree -0.155 Anomalous
org.axonframework.common.BuilderUtils axon-messaging-4.12.2 0.0569 Hub articleRank -0.2124 degree -0.1786 pageRank -0.1674 Anomalous
org.axonframework.commandhandling.CommandMessage axon-messaging-4.12.2 0.0568 articleRank -0.2191 degree -0.184 pageRank -0.1827 Anomalous
org.axonframework.springboot.autoconfig.AxonAutoConfiguration axon-spring-boot-autoconfigure-4.12.2 0.0535 Bottleneck degree -0.2783 outgoingDependencies -0.1622 betweenness -0.1214 Anomalous
org.axonframework.messaging.unitofwork.UnitOfWork axon-messaging-4.12.2 0.0512 Bottleneck articleRank -0.1984 degree -0.1927 pageRank -0.1725 Anomalous
org.axonframework.messaging.ScopeDescriptor axon-messaging-4.12.2 0.0484 articleRank -0.2083 pageRank -0.2001 pageToArticleRankDifference -0.1459 Anomalous
org.axonframework.eventhandling.TrackedEventMessage axon-messaging-4.12.2 0.0438 articleRank -0.229 pageRank -0.1908 degree -0.1815 Anomalous
org.axonframework.common.AxonException axon-messaging-4.12.2 0.0415 Authority articleRank -0.2523 pageRank -0.25 pageToArticleRankDifference -0.1948 Anomalous
org.axonframework.common.AxonNonTransientException axon-messaging-4.12.2 0.0405 Hub articleRank -0.2665 pageRank -0.2521 pageToArticleRankDifference -0.1693 Anomalous
org.axonframework.tracing.SpanFactory axon-messaging-4.12.2 0.0395 articleRank -0.2242 degree -0.2132 pageRank -0.1955 Anomalous
org.axonframework.serialization.Converter axon-messaging-4.12.2 0.0388 pageRank -0.2347 articleRank -0.2162 pageToArticleRankDifference -0.1752 Anomalous
org.axonframework.tracing.Span axon-messaging-4.12.2 0.0375 articleRank -0.2193 pageRank -0.1807 degree -0.1657 Anomalous
org.axonframework.common.stream.BlockingStream axon-messaging-4.12.2 0.0371 pageRank -0.2003 pageToArticleRankDifference -0.1805 nodeEmbeddingPCA_21 -0.0465 Anomalous

Visualizations

See Plot Interpretation Guide on how to read the plots in detail.

Anomalies

Anomalies

Global feature importance SHAP summary plots

Anomaly feature importance explained (global)

Feature dependence plots for top important features

Anomaly feature dependence explained (global)

Local SHAP Force Plots – Top 6 Anomalies

Top 1 anomaly - local feature importance Top 2 anomaly - local feature importance Top 3 anomaly - local feature importance Top 4 anomaly - local feature importance Top 5 anomaly - local feature importance Top 6 anomaly - local feature importance

Cluster Diagnostics

Clusters largest average radius Clusters largest max radius Clusters largest size

Cluster Membership Strength

Cluster probabilities

Cluster Noise and Bridge Analysis

Cluster Noise: Highly central and popular Cluster Noise: Poorly integrated bridges Cluster Noise: Role inverted bridges

Feature Distributions

Betweenness Centrality Distribution Clustering coefficient distribution PageRank minus ArticleRank distribution

Feature Relationships

Clustering coefficient versus PageRank

Graph Visualizations

TopHub Graph Visualizations

TopHub 1

TopHub 2

TopHub 3

TopHub 4

TopHub 5

TopBottleneck Graph Visualizations

TopBottleneck 1

TopBottleneck 2

TopBottleneck 3

TopBottleneck 4

TopBottleneck 5

TopAuthority Graph Visualizations

TopAuthority 1

TopAuthority 2

TopAuthority 3

TopAuthority 4

TopAuthority 5

TopBridge Graph Visualizations

TopBridge 1

TopBridge 2

TopBridge 3

TopBridge 4

TopBridge 5

TopOutlier Graph Visualizations

TopOutlier 1

TopOutlier 2

--

3. Plot Interpretation Guide

Purpose: Understand each plot type’s diagnostic value.
Applies to: All abstraction levels.

Plot Type Best For Adds Why It Matters
Anomalies Plot Seeing distribution of anomalies in clusters Context of clusters & outliers Reveals isolation or cluster-based anomalies
SHAP Summary Global feature importance Feature impact direction Shows what drives anomalies overall
Local SHAP Force Explaining a single anomaly Feature contribution breakdown Useful for debugging individual outliers
Dependence Plot Understanding feature influence Interaction visualization Reveals nonlinear feature effects
Cluster Metrics Cluster characteristics Radius, cohesion, noise Identifies weakly defined or noisy clusters

3. Plot Interpretation Guide

Purpose: Provide a direct mapping between all plots and their analytical meaning.
Scope: Applies to plots for Java Type, Java Package, and similar abstraction levels.
Format: Each entry includes Best for, Adds, and Why, matching the in-report descriptions.

📘 Main Plots

Plot Description Best For Adds Why
Anomalies 2D visualization of all code units showing clusters and anomalies. Understanding the overall distribution of anomalies in relation to clusters. Context of clusters and outliers. Reveals whether anomalies are isolated or cluster-based, guiding investigation.
Global Feature Importance (SHAP Summary) Mean absolute SHAP values ranking global feature impact. Global understanding of which features drive anomalies. Direction of impact (color shows feature value). Explains which metrics consistently influence anomaly detection.
Feature Dependence (Top Important Features) Shows how specific feature values affect anomaly score; colored by interacting feature. Understanding how one feature affects anomaly scores. Color shows feature interaction or threshold effect. Helps identify nonlinear relationships and feature interactions.

📙 Local Explanation Plots

Plot Description Best For Adds Why
Local SHAP Force Plots (Top Anomalies 1–6) Visualizes per-feature contributions to each anomaly’s score relative to baseline. Explaining why a specific data point is anomalous. Visual breakdown of how each feature contributes to anomaly score. Enables debugging of individual anomalies through transparent explanation.

📗 Cluster-Level Diagnostic Plots

Plot Description Best For Adds Why
Clusters – Overall Shows all clusters since they all fit into one plot. Gaining a holistic view of cluster characteristics in the dataset. An overall summary of how all clusters are distributed and their key metrics. Understanding the general structure and properties of clusters can help identify patterns and potential anomalies in the data.
Clusters – Largest Average Radius Ranks clusters by mean distance of members from their centroid. Getting an overview of clusters that are more dispersed. Identifies clusters with internal variability. Large average radius suggests less cohesion and potential outliers.
Clusters – Largest Max Radius Shows clusters with the farthest outlying member. Identifying clusters that have members farthest from cluster center. Highlights clusters containing extreme outliers. Indicates clusters that may contain hidden anomalies.
Clusters – Largest Size Displays cluster membership counts. Understanding which clusters contain the most code units. Provides sense of frequency of code structures. Large clusters may represent common design patterns; small clusters are specialized.
Cluster Probabilities Distribution of HDBSCAN membership probabilities. Detecting code units that don’t strongly belong to any cluster. Measures how well-defined clusters are. Highlights noisy or weakly defined clusters.

📒 Cluster Noise & Bridge Diagnostics

Plot Description Best For Adds Why
Cluster Noise – Highly Central and Popular Central nodes that don’t fit any cluster. Detecting code units that are highly connected but anomalous. Reveals influential but misfit nodes. Such nodes may be key but unstable integration points.
Cluster Noise – Poorly Integrated Bridges Nodes connecting clusters but weakly integrated. Detecting code units that bridge modules unusually. Identifies cross-cutting or leaking dependencies. May reveal architectural boundary violations.
Cluster Noise – Role Inverted Bridges Bridges with reversed structural roles compared to expected topology. Detecting code units connecting clusters in unexpected ways. Highlights anomalous coupling roles. Indicates architectural inversion or misuse of interfaces.

📙 Feature Distribution & Relationship Plots

Plot Description Best For Adds Why
Betweenness Centrality Distribution Histogram of betweenness values. Identifying code units that act as structural bridges. Insight into flow of dependency control. Detects potential bottlenecks or single points of failure.
Clustering Coefficient Distribution Histogram of local clustering coefficients. Identifying modularity and local cohesion. Insight into how tightly code units cluster. Reveals how cohesive or isolated different regions of the graph are.
PageRank – ArticleRank Difference Distribution Distribution of PageRank - ArticleRank. Identifying influential nodes beyond local connectivity. Shows imbalance between influence and popularity. Highlights components with disproportionate architectural impact.
Clustering Coefficient vs PageRank Scatterplot comparing local clustering to global influence. Identifying relationships between cohesion and centrality. Visualizes trade-offs between modularity and reach. Helps spot code units that are both locally and globally critical.

📕 Graph Visualizations (Archetype-Level Network Views)

Plot Description Best For Adds Why
Top Hub Graph Visualization Displays the most connected node (e.g., #1 Hub) at the center, surrounded by its direct dependencies. Incoming nodes show who is dependent on the hub. Understanding highly connected code units or components that serve as central integrators. Highlights nodes that act as major dependency aggregators. Helps detect over-centralized modules or potential architectural bottlenecks.
Top Bottleneck Graph Visualization Shows the node with the highest betweenness centrality (e.g., #1 Bottleneck) and its local neighborhood. Identifying code units that control information or dependency flow. Emphasizes nodes that mediate critical paths between modules. Reveals single points of failure or routing constraints in dependency flow.
Top Authority Graph Visualization Centers the most authoritative node (e.g., #1 Authority) with incoming and outgoing links from dependent nodes with high PageRank and emphasized PageRank to ArticleRank difference. Detecting key knowledge or functionality providers. Highlights components with high centrality. Indicates structural or semantic “sources of truth” in the system.
Top Bridge Graph Visualization Displays a node acting as a structural bridge between clusters (e.g., #1 Bridge) and its cross-cluster connections based on node embeddings encoding the Graph structure. Understanding cross-cutting dependencies between modules. Reveals links connecting distinct architectural domains. Useful for spotting boundary leaks or undesired coupling between subsystems.
Top Outlier Graph Visualization Centers an unusual or isolated node (e.g., #1 Outlier) that can hardly be assigned to a cluster and visualizes its sparse or unexpected dependency patterns. Identifying structurally or behaviorally anomalous nodes. Highlights nodes with rare or unexpected connection patterns. Helps pinpoint code units that deviate from established dependency norms.

Note:

  • In all Graph Visualizations, the central node represents the selected Top Archetype (e.g., Top 1 Hub).
  • Darker nodes indicate incoming dependencies, while brighter nodes indicate outgoing dependencies.
  • Emphasized nodes (thicker borders or larger size) mark particularly influential or anomalous dependencies, depending on the archetype.
  • These visualizations are most effective for interpreting local dependency topology and role significance of key components.

📔 Summary Categories

Category Included Plots Typical Usage
Main Diagnostic Anomalies, Global SHAP, Feature Dependence High-level anomaly review
Local Explanation Local SHAP Force Plots Case-by-case anomaly debugging
Cluster Diagnostics Cluster Radius / Size / Probability Assess cluster cohesion and outliers
Cluster Noise Analysis Cluster Noise (3 types) Identify special structural anomalies
Feature Distributions Betweenness, Clustering, Rank Difference Assess feature-based structure patterns
Feature Relationships Clustering vs PageRank Evaluate global vs local influence balance
Archetype Graphs Top Hub / Bottleneck / Authority / Bridge / Outlier Visualizing key dependency roles and structural importance

💡 Reading Guidance

  • Color Conventions:
    Red = anomalous, Green = typical, Light grey = noise, Pale colors = clusters.
  • Scales:
    SHAP values are normalized (mean absolute); graph metrics standardized by z-score.
  • How to Use:
    1. Start with Main Diagnostic plots to identify anomalies and drivers.
    2. Use Local SHAP for detailed case analysis.
    3. Check Cluster Diagnostics and Noise Plots to verify grouping quality.
    4. Use Feature Distributions to contextualize metrics.
    5. Cross-reference Feature Relationships for architectural interpretation.

📄 Structured Form (YAML Summary)

You can include this in your appendix for machine-readable mapping:

plots:
  main:
    - name: Anomalies
      purpose: Distribution of anomalies and clusters
    - name: Global Feature Importance (SHAP)
      purpose: Global feature ranking
    - name: Feature Dependence
      purpose: Feature–score relationship
  local:
    - name: Local SHAP Force Plots
      purpose: Local explanations for top anomalies
  cluster:
    - name: Clusters Largest Average Radius
      purpose: Identify dispersed clusters
    - name: Clusters Largest Max Radius
      purpose: Identify extreme outlier clusters
    - name: Clusters Largest Size
      purpose: Identify dominant cluster types
    - name: Cluster Probabilities
      purpose: Assess cluster definition strength
  cluster_noise:
    - name: Cluster Noise – Highly Central and Popular
      purpose: Central anomalies without cluster fit
    - name: Cluster Noise – Poorly Integrated Bridges
      purpose: Weakly integrated bridges
    - name: Cluster Noise – Role Inverted Bridges
      purpose: Inverted bridge roles
  feature_distributions:
    - name: Betweenness Centrality Distribution
      purpose: Bridge and bottleneck detection
    - name: Clustering Coefficient Distribution
      purpose: Cohesion and modularity measurement
    - name: PageRank – ArticleRank Difference Distribution
      purpose: Influence vs popularity analysis
  feature_relationships:
    - name: Clustering Coefficient vs PageRank
      purpose: Local vs global influence comparison

4. Taxonomy of Anomaly Archetypes

Archetype Feature Profile Architectural Risk
Hub High degree, low clustering coefficient Central dependency; fragile hotspot
Bottleneck High betweenness, low redundancy Single point of failure; slows evolution
Outlier High cluster distance, small cluster size Misfit or irregular dependency pattern
Authority High PageRank, low ArticleRank Over-relied utility; low local stability
Bridge Cross-cluster connection Risky coupling; weak modular boundaries

Structured form (for LLM parsing):

archetypes:
  - name: Hub
    profile: High degree, low clustering coefficient
    risk: Central dependency, fragile hotspot
  - name: Bottleneck
    profile: High betweenness, low redundancy
    risk: Single point of failure
  - name: Outlier
    profile: High cluster distance, small cluster size
    risk: Misfit component
  - name: Authority
    profile: High PageRank, low ArticleRank
    risk: Over-relied utility
  - name: Bridge
    profile: Cross-cluster connector
    risk: Risky coupling

5. Recommendations

  • Refactor hubs: Decompose large or over-connected utilities.
  • Mitigate bottlenecks: Introduce redundancy or alternative communication paths.
  • Investigate outliers: Determine if anomalies are justified exceptions.
  • Raise cohesion: Increase local clustering by improving modular boundaries.
  • Stabilize authorities: Encapsulate frequently used but fragile components.
  • Validate bridges: Confirm cross-cluster connectors are intentional and safe.

6. Appendix

6.1 Methodology Overview

  1. Build dependency graph (types, packages, artifacts).
  2. Compute graph metrics: degree, PageRank, betweenness, clustering coefficient, etc.
  3. Generate embeddings via Fast Random Projection.
  4. Reduce embeddings with PCA (retain 90% variance).
  5. Train Isolation Forest for anomaly detection.
  6. Explain results using SHAP (via Random Forest proxy).
  7. Cluster anomalies via HDBSCAN, tuned with Leiden reference communities (AMI score).
  8. Hyperparameter optimization for both Isolation Forest and Random Forest proxy with their F1 score

6.2 Feature Set

  • Degree (in/out)
  • PageRank
  • ArticleRank
  • Page-to-Article Rank Difference
  • Betweenness Centrality
  • Local Clustering Coefficient
  • Cluster Outlier Score (1.0 - cluster probability)
  • Cluster Radius (avg, max)
  • Cluster Size
  • Node Embedding (PCA 20–35 dims)

6.3 Architecture Diagram

Anomaly Detection Architecture